{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 本周内容：分进合击进阶分析策略\n",
    "1. 数据透视：pivot,pivot_table系统\n",
    "2. 数据重塑：reshape\n",
    "3. 数据框排序： sort_values,sori_index\n",
    "4. 数据表从长到宽"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本周知识点主要在二维项度上，以“类别”分进，以“数量”合击，主要内容包括：\n",
    "1. 分进合击进阶分析策略\n",
    "   * 什么是pivot,pivot_table,cross_tab数据透序表\n",
    "2. 复习分进合击的心法以及剑法学习\n",
    "   * 区分变量性质(variable types)/数据形态(data types)\n",
    "   * 分进多为“类别”，可以是定类或定序\n",
    "   * 合击常为“数量”，可以是定距或定比\n",
    "3. 二维项度的设定决定及规划\n",
    "   * 使用nuninque预判生成较宽表格的大小\n",
    "   * 多层次下的较复杂排序\n",
    "   * 数据框“长”“宽”的使用差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>710</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>11330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">以色列</th>\n",
       "      <th>2002</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>210</td>\n",
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       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <th>2014</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">印度</th>\n",
       "      <th>2000</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">美国</th>\n",
       "      <th>2006</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <th>2016</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">英国</th>\n",
       "      <th>2004</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>2015</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>2011</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "    2005       300\n",
       "    2006      2380\n",
       "    2007      1280\n",
       "    2008       710\n",
       "    2009       950\n",
       "    2010      1590\n",
       "    2011      6570\n",
       "    2012     11330\n",
       "    2013      5340\n",
       "    2014     16150\n",
       "    2015      3960\n",
       "    2016      1300\n",
       "    2017       810\n",
       "    2018      1090\n",
       "    2019       100\n",
       "以色列 2002       150\n",
       "    2010       210\n",
       "    2011       150\n",
       "    2012       150\n",
       "    2013        70\n",
       "卢森堡 2014        70\n",
       "印度  2000       150\n",
       "    2004       150\n",
       "    2007        70\n",
       "    2008       840\n",
       "...            ...\n",
       "美国  2006       810\n",
       "    2007      2650\n",
       "    2008      4570\n",
       "    2009      2090\n",
       "    2010      6150\n",
       "    2011      2620\n",
       "    2012      4670\n",
       "    2013      3840\n",
       "    2014      2220\n",
       "    2015      5980\n",
       "    2016      1690\n",
       "    2017       870\n",
       "    2019        70\n",
       "芬兰  2016        70\n",
       "英国  2004       350\n",
       "    2005       150\n",
       "    2009        70\n",
       "    2011       550\n",
       "    2012       450\n",
       "    2013       350\n",
       "    2015       350\n",
       "    2016       150\n",
       "菲律宾 2015        70\n",
       "西班牙 2011        70\n",
       "阿根廷 2013        70\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分进合击初阶分析策略，使用groupby agg\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn.tsv\",encoding = \"utf8\",sep = \"\\t\")\n",
    "data_初阶 = df.groupby(by = [\"国家\",\"成立年份\"]).agg({\"估值（亿人民币）\":\"sum\"})\n",
    "data_初阶\n",
    "# 此时的数据只有一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 20)\n"
     ]
    },
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       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分进合击进阶分析策略，使用groupby agg pivot\n",
    "data_进阶_pv = df.groupby(by=[\"国家\",\"成立年份\"]) \\\n",
    "                 .agg({\"估值（亿人民币）\":\"sum\"}) \\\n",
    "                 .reset_index() \\\n",
    "                 .set_index(\"国家\") \\\n",
    "                 .pivot(columns=\"成立年份\",values=\"估值（亿人民币）\")\n",
    "print(data_进阶_pv.shape)\n",
    "data_进阶_pv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set_index 和 reset_index \n",
    "1. set_index 可以设置单索引和复合索引，其参数append添加新索引\n",
    "2. reset_index 可以还原索引，重新变为默认为整型索引，即进行默认索引，取消groupby分组，将组别变成普通的列。其参数level控制了具体要还原的那个等级的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>国家</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>以色列</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>卢森堡</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>印度</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>哥伦比亚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>巴西</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>德国</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>新加坡</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>日本</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>法国</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>爱尔兰</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>爱沙尼亚</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>瑞典</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>瑞士</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>美国</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>芬兰</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>英国</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>西班牙</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>阿根廷</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>韩国</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>马耳他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份     国家   2000   2001    2002   2003    2004   2005    2006    2007  \\\n",
       "0        中国  170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   \n",
       "1       以色列    NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN   \n",
       "2       卢森堡    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "3        印度  150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   \n",
       "4     印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "5      哥伦比亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "6        巴西    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "7        德国  150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "8       新加坡    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "9        日本    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "10       法国    NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN   \n",
       "11     澳大利亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "12      爱尔兰  150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "13     爱沙尼亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "14       瑞典    NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN   \n",
       "15       瑞士    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "16       美国  210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0   \n",
       "17       芬兰    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "18       英国    NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN   \n",
       "19      菲律宾    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "20      西班牙    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "21      阿根廷    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "22       韩国    NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0   \n",
       "23      马耳他    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2008  ...    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "0      710.0  ...  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "1        NaN  ...   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "2        NaN  ...     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "3      840.0  ...  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "4        NaN  ...   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "5        NaN  ...     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "6        NaN  ...     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "7        NaN  ...     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "8        NaN  ...     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "9        NaN  ...     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "10       NaN  ...     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "11       NaN  ...     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "12       NaN  ...     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "13       NaN  ...     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "14       NaN  ...     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "15       NaN  ...     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "16    4570.0  ...  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "17       NaN  ...     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "18       NaN  ...     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "19       NaN  ...     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "20       NaN  ...     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "21       NaN  ...     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "22       NaN  ...   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "23       NaN  ...     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份   2017    2018   2019  \n",
       "0     810.0  1090.0  100.0  \n",
       "1       NaN     NaN    NaN  \n",
       "2       NaN     NaN    NaN  \n",
       "3      70.0     NaN    NaN  \n",
       "4       NaN     NaN    NaN  \n",
       "5       NaN     NaN    NaN  \n",
       "6       NaN     NaN    NaN  \n",
       "7       NaN     NaN    NaN  \n",
       "8       NaN     NaN    NaN  \n",
       "9       NaN     NaN    NaN  \n",
       "10      NaN     NaN    NaN  \n",
       "11      NaN     NaN    NaN  \n",
       "12      NaN     NaN    NaN  \n",
       "13      NaN     NaN    NaN  \n",
       "14      NaN     NaN    NaN  \n",
       "15      NaN     NaN    NaN  \n",
       "16    870.0     NaN   70.0  \n",
       "17      NaN     NaN    NaN  \n",
       "18      NaN     NaN    NaN  \n",
       "19      NaN     NaN    NaN  \n",
       "20      NaN     NaN    NaN  \n",
       "21      NaN     NaN    NaN  \n",
       "22      NaN     NaN    NaN  \n",
       "23    150.0     NaN    NaN  \n",
       "\n",
       "[24 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_进阶_pv.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
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       "      <th>2013</th>\n",
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       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分进合击进阶分析策略，使用pivot_table\n",
    "data_进阶_pv_table = df.pivot_table(index=\"国家\",columns=\"成立年份\", \\\n",
    "                        values = \"估值（亿人民币）\",aggfunc=\"sum\")\n",
    "print(data_进阶_pv_table.shape)\n",
    "data_进阶_pv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 什么是透视表？\n",
    "透视表是一种对数据动态排布并且分类汇总的表格格式，在pandas中它被称为pivot_table\n",
    "### 2. 每个pivot_table必须拥有一个index\n",
    "### 3. values可以对需要计算的数据进行筛选\n",
    "### 4. aggfunc参数可以设置我们对数据聚合时进行的函数操作\n",
    "### 5. columns类似index，可以设置列层次字段，它不是必要参数，但可以做为一种分割数据的可选方式\n",
    "   * fill_value填充空值，margins=True进行汇总\n",
    "   * aggfunc也可以使用dict类型，如果dict中的内容与values不匹配时，以dict中为准\n",
    "   \n",
    "### 6.  pivot和pivot_table的区别\n",
    "   * pivot重点在于reshape，就是合并同类项。在行与列的交叉点值的索引应该是唯一值，不是唯一值就会报错，尽量使用pivot_table，可以避免这个问题\n",
    "   * 在调用pivot函数之前，必须确保我们指定的列和行没有重复的数据，如果无法确定，可以使用pivot_table\n",
    "   * 实际上，pivot_table是pivot的泛化，它允许在数据集中聚合具有相同目标的多个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['中国', '美国', '印度', '英国', '印度尼西亚', '韩国', '新加坡', '德国', '以色列', '瑞士', '巴西',\n",
       "       '瑞典', '法国', '日本', '澳大利亚', '爱尔兰', '马耳他', '爱沙尼亚', '芬兰', '哥伦比亚', '菲律宾',\n",
       "       '西班牙', '卢森堡', '阿根廷'],\n",
       "      dtype='object', name='国家')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分进合击进阶 pivot_table,按总值排序\n",
    "# sort_values\n",
    "排序 = data_进阶_pv_table.sum(axis=1).sort_values(ascending=False).index\n",
    "排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['中国', '美国', '印度', '英国', '印度尼西亚', '韩国', '新加坡', '德国', '以色列', '瑞士', '巴西',\n",
       "       '瑞典', '法国', '日本', '澳大利亚', '爱尔兰', '马耳他', '爱沙尼亚', '芬兰', '哥伦比亚', '菲律宾',\n",
       "       '西班牙', '卢森堡', '阿根廷'],\n",
       "      dtype='object', name='国家')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "排序_总值 = data_进阶_pv_table.sum(axis = 1).sort_values(ascending=False).index\n",
    "排序_总值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "美国     870.0     NaN   70.0  \n",
       "印度      70.0     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "以色列      NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "分进合击进阶_排序 = data_进阶_pv_table.loc[排序]\n",
    "分进合击进阶_排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>总值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>印度</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>英国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>印度尼西亚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>韩国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新加坡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>德国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>以色列</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>瑞士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>巴西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>瑞典</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>法国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>日本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>澳大利亚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>爱尔兰</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>马耳他</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>爱沙尼亚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芬兰</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>哥伦比亚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>菲律宾</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>西班牙</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>卢森堡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>阿根廷</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009  ...    2011     2012    2013     2014    2015    2016   2017  \\\n",
       "国家             ...                                                            \n",
       "中国      950.0  ...  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0  810.0   \n",
       "美国     2090.0  ...  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0  870.0   \n",
       "印度        NaN  ...   440.0    140.0   350.0    270.0     NaN    70.0   70.0   \n",
       "英国       70.0  ...   550.0    450.0   350.0      NaN   350.0   150.0    NaN   \n",
       "印度尼西亚   500.0  ...    70.0    300.0     NaN      NaN     NaN     NaN    NaN   \n",
       "韩国        NaN  ...   270.0      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "新加坡       NaN  ...     NaN   1350.0     NaN      NaN     NaN     NaN    NaN   \n",
       "德国       70.0  ...   150.0    370.0   200.0     70.0     NaN     NaN    NaN   \n",
       "以色列       NaN  ...   150.0    150.0    70.0      NaN     NaN     NaN    NaN   \n",
       "瑞士        NaN  ...     NaN     70.0     NaN    500.0   150.0     NaN    NaN   \n",
       "巴西        NaN  ...    70.0     70.0   370.0      NaN     NaN     NaN    NaN   \n",
       "瑞典        NaN  ...     NaN      NaN     NaN      NaN     NaN   150.0    NaN   \n",
       "法国        NaN  ...     NaN      NaN    70.0      NaN     NaN    70.0    NaN   \n",
       "日本        NaN  ...     NaN      NaN     NaN    220.0     NaN     NaN    NaN   \n",
       "澳大利亚      NaN  ...     NaN    200.0     NaN      NaN     NaN     NaN    NaN   \n",
       "爱尔兰       NaN  ...     NaN      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "马耳他       NaN  ...     NaN      NaN     NaN      NaN     NaN     NaN  150.0   \n",
       "爱沙尼亚      NaN  ...     NaN      NaN    70.0      NaN     NaN     NaN    NaN   \n",
       "芬兰        NaN  ...     NaN      NaN     NaN      NaN     NaN    70.0    NaN   \n",
       "哥伦比亚      NaN  ...     NaN      NaN     NaN      NaN     NaN    70.0    NaN   \n",
       "菲律宾       NaN  ...     NaN      NaN     NaN      NaN    70.0     NaN    NaN   \n",
       "西班牙       NaN  ...    70.0      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "卢森堡       NaN  ...     NaN      NaN     NaN     70.0     NaN     NaN    NaN   \n",
       "阿根廷       NaN  ...     NaN      NaN    70.0      NaN     NaN     NaN    NaN   \n",
       "\n",
       "成立年份     2018   2019     总值  \n",
       "国家                           \n",
       "中国     1090.0  100.0     中国  \n",
       "美国        NaN   70.0     美国  \n",
       "印度        NaN    NaN     印度  \n",
       "英国        NaN    NaN     英国  \n",
       "印度尼西亚     NaN    NaN  印度尼西亚  \n",
       "韩国        NaN    NaN     韩国  \n",
       "新加坡       NaN    NaN    新加坡  \n",
       "德国        NaN    NaN     德国  \n",
       "以色列       NaN    NaN    以色列  \n",
       "瑞士        NaN    NaN     瑞士  \n",
       "巴西        NaN    NaN     巴西  \n",
       "瑞典        NaN    NaN     瑞典  \n",
       "法国        NaN    NaN     法国  \n",
       "日本        NaN    NaN     日本  \n",
       "澳大利亚      NaN    NaN   澳大利亚  \n",
       "爱尔兰       NaN    NaN    爱尔兰  \n",
       "马耳他       NaN    NaN    马耳他  \n",
       "爱沙尼亚      NaN    NaN   爱沙尼亚  \n",
       "芬兰        NaN    NaN     芬兰  \n",
       "哥伦比亚      NaN    NaN   哥伦比亚  \n",
       "菲律宾       NaN    NaN    菲律宾  \n",
       "西班牙       NaN    NaN    西班牙  \n",
       "卢森堡       NaN    NaN    卢森堡  \n",
       "阿根廷       NaN    NaN    阿根廷  \n",
       "\n",
       "[24 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "分进合击进阶_排序['总值'] = 排序_总值\n",
    "分进合击进阶_排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
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       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据框排序 按列索引进行排序 sort_index,axis=0\n",
    "data_进阶_pv_table.sort_index(axis=0,ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort_index,axis=1\n",
    "data_进阶_pv_table.sort_index(axis=1,ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort_values 参数说明\n",
    "# axis =0 表示纵轴，方向从上到下\n",
    "# axis =1 表示横轴，方向从左到右。数组的变化是横向的，而体现出来的是列的增加或减少\n",
    "# ascending 是否按指定列的数组升序排列，默认为True，即升序排列\n",
    "# inplace 是否用排序后的数据集替换原来的数据，默认为False，即不替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>2000</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>2001</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国</td>\n",
       "      <td>2002</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国</td>\n",
       "      <td>2003</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中国</td>\n",
       "      <td>2005</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>中国</td>\n",
       "      <td>2006</td>\n",
       "      <td>2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>中国</td>\n",
       "      <td>2007</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>中国</td>\n",
       "      <td>2008</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>中国</td>\n",
       "      <td>2009</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>中国</td>\n",
       "      <td>2010</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>中国</td>\n",
       "      <td>2011</td>\n",
       "      <td>6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>中国</td>\n",
       "      <td>2012</td>\n",
       "      <td>11330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>中国</td>\n",
       "      <td>2013</td>\n",
       "      <td>5340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>中国</td>\n",
       "      <td>2014</td>\n",
       "      <td>16150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>中国</td>\n",
       "      <td>2015</td>\n",
       "      <td>3960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>中国</td>\n",
       "      <td>2016</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>中国</td>\n",
       "      <td>2017</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>中国</td>\n",
       "      <td>2018</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>中国</td>\n",
       "      <td>2019</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>以色列</td>\n",
       "      <td>2002</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>以色列</td>\n",
       "      <td>2010</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>以色列</td>\n",
       "      <td>2011</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>以色列</td>\n",
       "      <td>2012</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>以色列</td>\n",
       "      <td>2013</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>卢森堡</td>\n",
       "      <td>2014</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>印度</td>\n",
       "      <td>2000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>印度</td>\n",
       "      <td>2004</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>印度</td>\n",
       "      <td>2007</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>印度</td>\n",
       "      <td>2008</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>美国</td>\n",
       "      <td>2006</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>美国</td>\n",
       "      <td>2007</td>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>美国</td>\n",
       "      <td>2008</td>\n",
       "      <td>4570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>美国</td>\n",
       "      <td>2009</td>\n",
       "      <td>2090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>美国</td>\n",
       "      <td>2010</td>\n",
       "      <td>6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>美国</td>\n",
       "      <td>2011</td>\n",
       "      <td>2620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>美国</td>\n",
       "      <td>2012</td>\n",
       "      <td>4670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>美国</td>\n",
       "      <td>2013</td>\n",
       "      <td>3840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>美国</td>\n",
       "      <td>2014</td>\n",
       "      <td>2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>美国</td>\n",
       "      <td>2015</td>\n",
       "      <td>5980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>美国</td>\n",
       "      <td>2016</td>\n",
       "      <td>1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>美国</td>\n",
       "      <td>2017</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>美国</td>\n",
       "      <td>2019</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>芬兰</td>\n",
       "      <td>2016</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>英国</td>\n",
       "      <td>2004</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>英国</td>\n",
       "      <td>2005</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>英国</td>\n",
       "      <td>2009</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>英国</td>\n",
       "      <td>2011</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>英国</td>\n",
       "      <td>2012</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>英国</td>\n",
       "      <td>2013</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>英国</td>\n",
       "      <td>2015</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>英国</td>\n",
       "      <td>2016</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>2015</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>西班牙</td>\n",
       "      <td>2011</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>阿根廷</td>\n",
       "      <td>2013</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2005</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2007</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2010</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2011</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>马耳他</td>\n",
       "      <td>2017</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     国家  成立年份  估值（亿人民币）\n",
       "0    中国  2000       170\n",
       "1    中国  2001       170\n",
       "2    中国  2002       200\n",
       "3    中国  2003       200\n",
       "4    中国  2004       100\n",
       "5    中国  2005       300\n",
       "6    中国  2006      2380\n",
       "7    中国  2007      1280\n",
       "8    中国  2008       710\n",
       "9    中国  2009       950\n",
       "10   中国  2010      1590\n",
       "11   中国  2011      6570\n",
       "12   中国  2012     11330\n",
       "13   中国  2013      5340\n",
       "14   中国  2014     16150\n",
       "15   中国  2015      3960\n",
       "16   中国  2016      1300\n",
       "17   中国  2017       810\n",
       "18   中国  2018      1090\n",
       "19   中国  2019       100\n",
       "20  以色列  2002       150\n",
       "21  以色列  2010       210\n",
       "22  以色列  2011       150\n",
       "23  以色列  2012       150\n",
       "24  以色列  2013        70\n",
       "25  卢森堡  2014        70\n",
       "26   印度  2000       150\n",
       "27   印度  2004       150\n",
       "28   印度  2007        70\n",
       "29   印度  2008       840\n",
       "..  ...   ...       ...\n",
       "70   美国  2006       810\n",
       "71   美国  2007      2650\n",
       "72   美国  2008      4570\n",
       "73   美国  2009      2090\n",
       "74   美国  2010      6150\n",
       "75   美国  2011      2620\n",
       "76   美国  2012      4670\n",
       "77   美国  2013      3840\n",
       "78   美国  2014      2220\n",
       "79   美国  2015      5980\n",
       "80   美国  2016      1690\n",
       "81   美国  2017       870\n",
       "82   美国  2019        70\n",
       "83   芬兰  2016        70\n",
       "84   英国  2004       350\n",
       "85   英国  2005       150\n",
       "86   英国  2009        70\n",
       "87   英国  2011       550\n",
       "88   英国  2012       450\n",
       "89   英国  2013       350\n",
       "90   英国  2015       350\n",
       "91   英国  2016       150\n",
       "92  菲律宾  2015        70\n",
       "93  西班牙  2011        70\n",
       "94  阿根廷  2013        70\n",
       "95   韩国  2005        70\n",
       "96   韩国  2007       350\n",
       "97   韩国  2010       670\n",
       "98   韩国  2011       270\n",
       "99  马耳他  2017       150\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_初阶.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>企业名称</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">中国</th>\n",
       "      <th>1919酒类直供</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58到家</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APUS</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
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       "      <th>Block.One</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>DotC United</th>\n",
       "      <td>70</td>\n",
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       "      <th>G7</th>\n",
       "      <td>70</td>\n",
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       "      <td>70</td>\n",
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       "      <th>PingPong</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>SheIn</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIPKID</th>\n",
       "      <td>200</td>\n",
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       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>WeLab</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>一下科技</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一点资讯</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一起作业</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>丁香园</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>万能钥匙</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>丰巢科技</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九次方大数据</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云从科技</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>云知声</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云鸟科技</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>京东健康</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>京东数科</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>京东物流</th>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人人贷</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人人车</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亿邦国际</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企鹅杏仁</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>优刻得</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">美国</th>\n",
       "      <th>letgo</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reddit</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sweetgreen</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图森未来</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小马智行</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱彼迎</th>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <th>HMD</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">英国</th>\n",
       "      <th>BenevolentAI</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Checkout.com</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>Deliveroo</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graphcore</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greensill</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improbable</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>Monzo</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OakNorth</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>Ovo Energy</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>Oxford Nanopore Technologies</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revolut</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>The Hut Group</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TransferWise</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>Revolution Precrafted</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>Cabify</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <th>Auth0</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">韩国</th>\n",
       "      <th>Bluehole</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupang</th>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMON</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toss</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woowa Brothers</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>Yanolja</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>Binance</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  估值（亿人民币）\n",
       "国家  企业名称                                  \n",
       "中国  1919酒类直供                            70\n",
       "    58到家                                70\n",
       "    APUS                               150\n",
       "    Block.One                          150\n",
       "    DotC United                         70\n",
       "    G7                                  70\n",
       "    Momenta                             70\n",
       "    PingPong                           150\n",
       "    SheIn                              100\n",
       "    VIPKID                             200\n",
       "    V领地                                 70\n",
       "    WeLab                               70\n",
       "    一下科技                               200\n",
       "    一点资讯                                70\n",
       "    一起作业                                70\n",
       "    丁香园                                 70\n",
       "    万能钥匙                                70\n",
       "    丰巢科技                                70\n",
       "    九次方大数据                              70\n",
       "    云从科技                               200\n",
       "    云知声                                 70\n",
       "    云鸟科技                                70\n",
       "    京东健康                               100\n",
       "    京东数科                              1300\n",
       "    京东物流                               800\n",
       "    人人贷                                 70\n",
       "    人人车                                 70\n",
       "    亿邦国际                               100\n",
       "    企鹅杏仁                                70\n",
       "    优刻得                                 70\n",
       "...                                    ...\n",
       "美国  letgo                               70\n",
       "    reddit                             200\n",
       "    sweetgreen                          70\n",
       "    图森未来                                70\n",
       "    小马智行                               150\n",
       "    爱彼迎                               2700\n",
       "芬兰  HMD                                 70\n",
       "英国  BenevolentAI                       150\n",
       "    Checkout.com                       150\n",
       "    Deliveroo                          150\n",
       "    Graphcore                          150\n",
       "    Greensill                          250\n",
       "    Improbable                         150\n",
       "    Monzo                              200\n",
       "    OakNorth                           200\n",
       "    Ovo Energy                          70\n",
       "    Oxford Nanopore Technologies       150\n",
       "    Revolut                            150\n",
       "    The Hut Group                      350\n",
       "    TransferWise                       300\n",
       "菲律宾 Revolution Precrafted               70\n",
       "西班牙 Cabify                              70\n",
       "阿根廷 Auth0                               70\n",
       "韩国  Bluehole                           350\n",
       "    Coupang                            600\n",
       "    TMON                                70\n",
       "    Toss                                70\n",
       "    Woowa Brothers                     200\n",
       "    Yanolja                             70\n",
       "马耳他 Binance                            150\n",
       "\n",
       "[494 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city = df.groupby(by=[\"国家\",\"企业名称\"]).agg({\"估值（亿人民币）\":\"sum\"})\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>10X Genomics</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919酒类直供</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23andMe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58到家</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APUS</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>About You</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actifio</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Affirm</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afiniti</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Learning</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtable</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allbirds</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphaeon Corporation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppDirect</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppLovin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argo AI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aurora</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auth0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto1 Group</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automation Anywhere</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automattic</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvidXchange</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Away</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BYJU’s</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BenevolentAI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BigBasket</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill.com</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BillDesk</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车好多</th>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车猫二手车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车置宝</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>转转</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软通动力</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辉能科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辣妈帮</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>达达-京东到家</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>返利网</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>远景能源</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>连连数字</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>途家网</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>途虎养车</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>酒仙网</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金山云</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融壹账通</th>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>银联商务</th>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿里体育</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陆金所</th>\n",
       "      <td>2700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>集奥聚合</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零氪科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零跑汽车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雾芯科技</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>首汽约车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马蜂窝</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>驴妈妈</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>驹马物流</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高顿</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>魔方公寓</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦奇教育科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "国家                        中国  以色列  卢森堡     印度  印度尼西亚  哥伦比亚  巴西     德国  新加坡  \\\n",
       "企业名称                                                                         \n",
       "10X Genomics             NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "1919酒类直供                70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "23andMe                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "58到家                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "APUS                   150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "About You                NaN  NaN  NaN    NaN    NaN   NaN NaN   70.0  NaN   \n",
       "Actifio                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Affirm                   NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Afiniti                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Age of Learning          NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Airtable                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Allbirds                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Alphaeon Corporation     NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AppDirect                NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AppLovin                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Argo AI                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Asana                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Aurora                   NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Auth0                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Auto1 Group              NaN  NaN  NaN    NaN    NaN   NaN NaN  300.0  NaN   \n",
       "Automation Anywhere      NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Automattic               NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Avant                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AvidXchange              NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Away                     NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BYJU’s                   NaN  NaN  NaN  400.0    NaN   NaN NaN    NaN  NaN   \n",
       "BenevolentAI             NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BigBasket                NaN  NaN  NaN   70.0    NaN   NaN NaN    NaN  NaN   \n",
       "Bill.com                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BillDesk                 NaN  NaN  NaN  150.0    NaN   NaN NaN    NaN  NaN   \n",
       "...                      ...  ...  ...    ...    ...   ...  ..    ...  ...   \n",
       "车好多                    600.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "车猫二手车                   70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "车置宝                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "转转                      70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "软通动力                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "辉能科技                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "辣妈帮                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "达达-京东到家                300.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "返利网                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "远景能源                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "连连数字                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "途家网                    100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "途虎养车                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "酒仙网                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "金山云                    150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "金融壹账通                  500.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "银联商务                   200.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "阿里体育                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "陆金所                   2700.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "集奥聚合                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "零氪科技                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "零跑汽车                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "雾芯科技                   150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "首汽约车                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "马蜂窝                    150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "驴妈妈                    100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "驹马物流                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "高顿                     100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "魔方公寓                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "麦奇教育科技                  70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "\n",
       "国家                    日本  ...  瑞典  瑞士     美国  芬兰     英国  菲律宾  西班牙   阿根廷  韩国  \\\n",
       "企业名称                      ...                                                 \n",
       "10X Genomics         NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "1919酒类直供             NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "23andMe              NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "58到家                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "APUS                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "About You            NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Actifio              NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Affirm               NaN  ... NaN NaN  200.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Afiniti              NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Age of Learning      NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Airtable             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Allbirds             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Alphaeon Corporation NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AppDirect            NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AppLovin             NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Argo AI              NaN  ... NaN NaN  500.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Asana                NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Aurora               NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Auth0                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN  70.0 NaN   \n",
       "Auto1 Group          NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Automation Anywhere  NaN  ... NaN NaN  200.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Automattic           NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Avant                NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AvidXchange          NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Away                 NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BYJU’s               NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BenevolentAI         NaN  ... NaN NaN    NaN NaN  150.0  NaN  NaN   NaN NaN   \n",
       "BigBasket            NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Bill.com             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BillDesk             NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "...                   ..  ...  ..  ..    ...  ..    ...  ...  ...   ...  ..   \n",
       "车好多                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "车猫二手车                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "车置宝                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "转转                   NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "软通动力                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "辉能科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "辣妈帮                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "达达-京东到家              NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "返利网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "远景能源                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "连连数字                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "途家网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "途虎养车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "酒仙网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "金山云                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "金融壹账通                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "银联商务                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "阿里体育                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "陆金所                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "集奥聚合                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "零氪科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "零跑汽车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "雾芯科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "首汽约车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "马蜂窝                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "驴妈妈                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "驹马物流                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "高顿                   NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "魔方公寓                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "麦奇教育科技               NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "\n",
       "国家                    马耳他  \n",
       "企业名称                       \n",
       "10X Genomics          NaN  \n",
       "1919酒类直供              NaN  \n",
       "23andMe               NaN  \n",
       "58到家                  NaN  \n",
       "APUS                  NaN  \n",
       "About You             NaN  \n",
       "Actifio               NaN  \n",
       "Affirm                NaN  \n",
       "Afiniti               NaN  \n",
       "Age of Learning       NaN  \n",
       "Airtable              NaN  \n",
       "Allbirds              NaN  \n",
       "Alphaeon Corporation  NaN  \n",
       "AppDirect             NaN  \n",
       "AppLovin              NaN  \n",
       "Argo AI               NaN  \n",
       "Asana                 NaN  \n",
       "Aurora                NaN  \n",
       "Auth0                 NaN  \n",
       "Auto1 Group           NaN  \n",
       "Automation Anywhere   NaN  \n",
       "Automattic            NaN  \n",
       "Avant                 NaN  \n",
       "AvidXchange           NaN  \n",
       "Away                  NaN  \n",
       "BYJU’s                NaN  \n",
       "BenevolentAI          NaN  \n",
       "BigBasket             NaN  \n",
       "Bill.com              NaN  \n",
       "BillDesk              NaN  \n",
       "...                   ...  \n",
       "车好多                   NaN  \n",
       "车猫二手车                 NaN  \n",
       "车置宝                   NaN  \n",
       "转转                    NaN  \n",
       "软通动力                  NaN  \n",
       "辉能科技                  NaN  \n",
       "辣妈帮                   NaN  \n",
       "达达-京东到家               NaN  \n",
       "返利网                   NaN  \n",
       "远景能源                  NaN  \n",
       "连连数字                  NaN  \n",
       "途家网                   NaN  \n",
       "途虎养车                  NaN  \n",
       "酒仙网                   NaN  \n",
       "金山云                   NaN  \n",
       "金融壹账通                 NaN  \n",
       "银联商务                  NaN  \n",
       "阿里体育                  NaN  \n",
       "陆金所                   NaN  \n",
       "集奥聚合                  NaN  \n",
       "零氪科技                  NaN  \n",
       "零跑汽车                  NaN  \n",
       "雾芯科技                  NaN  \n",
       "首汽约车                  NaN  \n",
       "马蜂窝                   NaN  \n",
       "驴妈妈                   NaN  \n",
       "驹马物流                  NaN  \n",
       "高顿                    NaN  \n",
       "魔方公寓                  NaN  \n",
       "麦奇教育科技                NaN  \n",
       "\n",
       "[494 rows x 24 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_city = city.reset_index().pivot(index=\"企业名称\",columns=\"国家\",values=\"估值（亿人民币）\")\n",
    "pivot_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>国家</th>\n",
       "      <th>中国</th>\n",
       "      <th>以色列</th>\n",
       "      <th>卢森堡</th>\n",
       "      <th>印度</th>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>哥伦比亚</th>\n",
       "      <th>巴西</th>\n",
       "      <th>德国</th>\n",
       "      <th>新加坡</th>\n",
       "      <th>日本</th>\n",
       "      <th>...</th>\n",
       "      <th>瑞典</th>\n",
       "      <th>瑞士</th>\n",
       "      <th>美国</th>\n",
       "      <th>芬兰</th>\n",
       "      <th>英国</th>\n",
       "      <th>菲律宾</th>\n",
       "      <th>西班牙</th>\n",
       "      <th>阿根廷</th>\n",
       "      <th>韩国</th>\n",
       "      <th>马耳他</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企业名称</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10X Genomics</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919酒类直供</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23andMe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58到家</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APUS</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>About You</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actifio</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Affirm</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afiniti</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Learning</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtable</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allbirds</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphaeon Corporation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppDirect</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppLovin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argo AI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aurora</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auth0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto1 Group</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automation Anywhere</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automattic</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvidXchange</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Away</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BYJU’s</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BenevolentAI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BigBasket</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill.com</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BillDesk</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车好多</th>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车猫二手车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车置宝</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>转转</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软通动力</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辉能科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辣妈帮</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>达达-京东到家</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>返利网</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>远景能源</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>连连数字</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>途家网</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>途虎养车</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>酒仙网</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金山云</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融壹账通</th>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>银联商务</th>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿里体育</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陆金所</th>\n",
       "      <td>2700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>集奥聚合</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零氪科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零跑汽车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雾芯科技</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>首汽约车</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马蜂窝</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>驴妈妈</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>驹马物流</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高顿</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>魔方公寓</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦奇教育科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "国家                        中国  以色列  卢森堡     印度  印度尼西亚  哥伦比亚  巴西     德国  新加坡  \\\n",
       "企业名称                                                                         \n",
       "10X Genomics             NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "1919酒类直供                70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "23andMe                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "58到家                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "APUS                   150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "About You                NaN  NaN  NaN    NaN    NaN   NaN NaN   70.0  NaN   \n",
       "Actifio                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Affirm                   NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Afiniti                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Age of Learning          NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Airtable                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Allbirds                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Alphaeon Corporation     NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AppDirect                NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AppLovin                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Argo AI                  NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Asana                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Aurora                   NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Auth0                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Auto1 Group              NaN  NaN  NaN    NaN    NaN   NaN NaN  300.0  NaN   \n",
       "Automation Anywhere      NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Automattic               NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Avant                    NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "AvidXchange              NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "Away                     NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BYJU’s                   NaN  NaN  NaN  400.0    NaN   NaN NaN    NaN  NaN   \n",
       "BenevolentAI             NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BigBasket                NaN  NaN  NaN   70.0    NaN   NaN NaN    NaN  NaN   \n",
       "Bill.com                 NaN  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "BillDesk                 NaN  NaN  NaN  150.0    NaN   NaN NaN    NaN  NaN   \n",
       "...                      ...  ...  ...    ...    ...   ...  ..    ...  ...   \n",
       "车好多                    600.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "车猫二手车                   70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "车置宝                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "转转                      70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "软通动力                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "辉能科技                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "辣妈帮                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "达达-京东到家                300.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "返利网                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "远景能源                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "连连数字                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "途家网                    100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "途虎养车                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "酒仙网                     70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "金山云                    150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "金融壹账通                  500.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "银联商务                   200.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "阿里体育                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "陆金所                   2700.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "集奥聚合                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "零氪科技                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "零跑汽车                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "雾芯科技                   150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "首汽约车                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "马蜂窝                    150.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "驴妈妈                    100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "驹马物流                    70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "高顿                     100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "魔方公寓                   100.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "麦奇教育科技                  70.0  NaN  NaN    NaN    NaN   NaN NaN    NaN  NaN   \n",
       "\n",
       "国家                    日本  ...  瑞典  瑞士     美国  芬兰     英国  菲律宾  西班牙   阿根廷  韩国  \\\n",
       "企业名称                      ...                                                 \n",
       "10X Genomics         NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "1919酒类直供             NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "23andMe              NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "58到家                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "APUS                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "About You            NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Actifio              NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Affirm               NaN  ... NaN NaN  200.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Afiniti              NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Age of Learning      NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Airtable             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Allbirds             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Alphaeon Corporation NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AppDirect            NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AppLovin             NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Argo AI              NaN  ... NaN NaN  500.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Asana                NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Aurora               NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Auth0                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN  70.0 NaN   \n",
       "Auto1 Group          NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Automation Anywhere  NaN  ... NaN NaN  200.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Automattic           NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Avant                NaN  ... NaN NaN  150.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "AvidXchange          NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Away                 NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BYJU’s               NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BenevolentAI         NaN  ... NaN NaN    NaN NaN  150.0  NaN  NaN   NaN NaN   \n",
       "BigBasket            NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "Bill.com             NaN  ... NaN NaN   70.0 NaN    NaN  NaN  NaN   NaN NaN   \n",
       "BillDesk             NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "...                   ..  ...  ..  ..    ...  ..    ...  ...  ...   ...  ..   \n",
       "车好多                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "车猫二手车                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "车置宝                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "转转                   NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "软通动力                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "辉能科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "辣妈帮                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "达达-京东到家              NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "返利网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "远景能源                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "连连数字                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "途家网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "途虎养车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "酒仙网                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "金山云                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "金融壹账通                NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "银联商务                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "阿里体育                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "陆金所                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "集奥聚合                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "零氪科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "零跑汽车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "雾芯科技                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "首汽约车                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "马蜂窝                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "驴妈妈                  NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "驹马物流                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "高顿                   NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "魔方公寓                 NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "麦奇教育科技               NaN  ... NaN NaN    NaN NaN    NaN  NaN  NaN   NaN NaN   \n",
       "\n",
       "国家                    马耳他  \n",
       "企业名称                       \n",
       "10X Genomics          NaN  \n",
       "1919酒类直供              NaN  \n",
       "23andMe               NaN  \n",
       "58到家                  NaN  \n",
       "APUS                  NaN  \n",
       "About You             NaN  \n",
       "Actifio               NaN  \n",
       "Affirm                NaN  \n",
       "Afiniti               NaN  \n",
       "Age of Learning       NaN  \n",
       "Airtable              NaN  \n",
       "Allbirds              NaN  \n",
       "Alphaeon Corporation  NaN  \n",
       "AppDirect             NaN  \n",
       "AppLovin              NaN  \n",
       "Argo AI               NaN  \n",
       "Asana                 NaN  \n",
       "Aurora                NaN  \n",
       "Auth0                 NaN  \n",
       "Auto1 Group           NaN  \n",
       "Automation Anywhere   NaN  \n",
       "Automattic            NaN  \n",
       "Avant                 NaN  \n",
       "AvidXchange           NaN  \n",
       "Away                  NaN  \n",
       "BYJU’s                NaN  \n",
       "BenevolentAI          NaN  \n",
       "BigBasket             NaN  \n",
       "Bill.com              NaN  \n",
       "BillDesk              NaN  \n",
       "...                   ...  \n",
       "车好多                   NaN  \n",
       "车猫二手车                 NaN  \n",
       "车置宝                   NaN  \n",
       "转转                    NaN  \n",
       "软通动力                  NaN  \n",
       "辉能科技                  NaN  \n",
       "辣妈帮                   NaN  \n",
       "达达-京东到家               NaN  \n",
       "返利网                   NaN  \n",
       "远景能源                  NaN  \n",
       "连连数字                  NaN  \n",
       "途家网                   NaN  \n",
       "途虎养车                  NaN  \n",
       "酒仙网                   NaN  \n",
       "金山云                   NaN  \n",
       "金融壹账通                 NaN  \n",
       "银联商务                  NaN  \n",
       "阿里体育                  NaN  \n",
       "陆金所                   NaN  \n",
       "集奥聚合                  NaN  \n",
       "零氪科技                  NaN  \n",
       "零跑汽车                  NaN  \n",
       "雾芯科技                  NaN  \n",
       "首汽约车                  NaN  \n",
       "马蜂窝                   NaN  \n",
       "驴妈妈                   NaN  \n",
       "驹马物流                  NaN  \n",
       "高顿                    NaN  \n",
       "魔方公寓                  NaN  \n",
       "麦奇教育科技                NaN  \n",
       "\n",
       "[494 rows x 24 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table_city = city.reset_index().pivot_table(index=\"企业名称\",columns=\"国家\",values=\"估值（亿人民币）\",aggfunc=\"sum\")\n",
    "pivot_table_city\n",
    "# pivot_table 比 pivot 多func运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28cc8d3c438>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化 plot\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams[\"font.sans-serif\"]=[\"Songti SC\"]\n",
    "pivot_city.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>11330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">以色列</th>\n",
       "      <th>2002</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <th>2014</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">印度</th>\n",
       "      <th>2000</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">美国</th>\n",
       "      <th>2006</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <th>2016</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">英国</th>\n",
       "      <th>2004</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>2015</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>2011</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "    2005       300\n",
       "    2006      2380\n",
       "    2007      1280\n",
       "    2008       710\n",
       "    2009       950\n",
       "    2010      1590\n",
       "    2011      6570\n",
       "    2012     11330\n",
       "    2013      5340\n",
       "    2014     16150\n",
       "    2015      3960\n",
       "    2016      1300\n",
       "    2017       810\n",
       "    2018      1090\n",
       "    2019       100\n",
       "以色列 2002       150\n",
       "    2010       210\n",
       "    2011       150\n",
       "    2012       150\n",
       "    2013        70\n",
       "卢森堡 2014        70\n",
       "印度  2000       150\n",
       "    2004       150\n",
       "    2007        70\n",
       "    2008       840\n",
       "...            ...\n",
       "美国  2006       810\n",
       "    2007      2650\n",
       "    2008      4570\n",
       "    2009      2090\n",
       "    2010      6150\n",
       "    2011      2620\n",
       "    2012      4670\n",
       "    2013      3840\n",
       "    2014      2220\n",
       "    2015      5980\n",
       "    2016      1690\n",
       "    2017       870\n",
       "    2019        70\n",
       "芬兰  2016        70\n",
       "英国  2004       350\n",
       "    2005       150\n",
       "    2009        70\n",
       "    2011       550\n",
       "    2012       450\n",
       "    2013       350\n",
       "    2015       350\n",
       "    2016       150\n",
       "菲律宾 2015        70\n",
       "西班牙 2011        70\n",
       "阿根廷 2013        70\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=[\"国家\",\"成立年份\"]).agg({\"估值（亿人民币）\":sum}).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>估值（亿人民币）</th>\n",
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       "      <th>2013</th>\n",
       "      <td>5340</td>\n",
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       "      <td>16150</td>\n",
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       "      <th>2015</th>\n",
       "      <td>3960</td>\n",
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       "      <th>2016</th>\n",
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       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <th>2014</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">印度</th>\n",
       "      <th>2000</th>\n",
       "      <td>150</td>\n",
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       "      <th>2004</th>\n",
       "      <td>150</td>\n",
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       "      <th>2008</th>\n",
       "      <td>840</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">美国</th>\n",
       "      <th>2006</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2650</td>\n",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4570</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2090</td>\n",
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       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6150</td>\n",
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       "      <td>2620</td>\n",
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       "      <th>2012</th>\n",
       "      <td>4670</td>\n",
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       "      <th>2013</th>\n",
       "      <td>3840</td>\n",
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       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2220</td>\n",
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       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5980</td>\n",
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       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1690</td>\n",
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       "      <td>870</td>\n",
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       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <th>2016</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">英国</th>\n",
       "      <th>2004</th>\n",
       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>2005</th>\n",
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       "    <tr>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>2015</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>2011</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <th>2013</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "    2005       300\n",
       "    2006      2380\n",
       "    2007      1280\n",
       "    2008       710\n",
       "    2009       950\n",
       "    2010      1590\n",
       "    2011      6570\n",
       "    2012     11330\n",
       "    2013      5340\n",
       "    2014     16150\n",
       "    2015      3960\n",
       "    2016      1300\n",
       "    2017       810\n",
       "    2018      1090\n",
       "    2019       100\n",
       "以色列 2002       150\n",
       "    2010       210\n",
       "    2011       150\n",
       "    2012       150\n",
       "    2013        70\n",
       "卢森堡 2014        70\n",
       "印度  2000       150\n",
       "    2004       150\n",
       "    2007        70\n",
       "    2008       840\n",
       "...            ...\n",
       "美国  2006       810\n",
       "    2007      2650\n",
       "    2008      4570\n",
       "    2009      2090\n",
       "    2010      6150\n",
       "    2011      2620\n",
       "    2012      4670\n",
       "    2013      3840\n",
       "    2014      2220\n",
       "    2015      5980\n",
       "    2016      1690\n",
       "    2017       870\n",
       "    2019        70\n",
       "芬兰  2016        70\n",
       "英国  2004       350\n",
       "    2005       150\n",
       "    2009        70\n",
       "    2011       550\n",
       "    2012       450\n",
       "    2013       350\n",
       "    2015       350\n",
       "    2016       150\n",
       "菲律宾 2015        70\n",
       "西班牙 2011        70\n",
       "阿根廷 2013        70\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"国家\",\"成立年份\"])[[\"估值（亿人民币）\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28cc9b469b0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 19969 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 39321 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22253 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27602 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 19969 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 39321 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22253 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:176: RuntimeWarning: Glyph 27602 missing from current font.\n",
      "  font.load_char(ord(s), flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20225 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21517 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20225 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21517 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33394 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21015 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21346 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26862 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22561 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23612 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24052 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21152 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22369 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29233 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23572 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27801 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20856 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33452 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33778 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23486 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38463 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26681 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24311 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38889 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 39532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32819 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20182 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33394 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21015 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21346 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26862 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22561 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23612 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24052 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21152 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22369 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29233 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23572 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27801 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20856 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33452 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33778 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23486 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38463 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26681 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24311 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38889 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 39532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32819 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20182 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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IISyxbFyNbew0DysrK1pfX1eSZNcGnHNaW1sbuwYAAACg2YpyYlo7RUUQG1Gv11Ov1xu7DgAAAKBdiu7ExLBRUU4MAAAAABUIwasjio2KIBYAAAAAKsCe2GoQxAIAAABABUJ3YmLYuAhiAQAAAKACRQaWKDYqglgAAAAAqEDoSkx34rjoThxRv9/X1taWut3sbk3TVKurq2PX+v3+HM8UAAAAQNWcH/2IOBobxH7sYx/TE088EfU2b7/9dt19991HHrO5uanl5WVJ0vb2tjY2NsauAQAAAGi2Yk8snZ2iopwYAAAAACrAlthqNDYTOyljCgAAAABVCglYErFxkYkFAAAAgAowYqcaBLEAAAAAUIEieCUVGxVBLAAAAABUwOXBK92J4yKIBQAAAIAKFHtiKSiOqrGNneZhZWVF6+vrSpLs2oBzTmtra2PXAAAAADQbjZ2qQRAbUa/XU6/XG7sOAAAAoF2Gc2LnfCINQzkxAAAAAFSAObHVIIgFAAAAgAoMy4kJY2MiiAUAAACACjjKiStBEAsAAAAAFRiWExPFxkQQCwAAAAAVoDtxNehOHFG/39fW1pa63exuTdNUq6urY9ckjV3v9/tzOXcAAAAAcRXdied8Hk3T2CD2a197hy5dfiDqbd54w2v0qlf93JHHbG5uanl5WZK0vb2tjY2NsWuHHQsAAACgGYpyYlKxUVFODAAAAAAVYE5sNRqbiZ2UMQUAAACAKrmwJ3a+p9E4ZGIBAAAAoALDTCxhbEwEsQAAAABQAb/vI+IgiAUAAACACjBipxoEsQAAAABQgeGIHaLYmBrb2GkeVlZWtL6+riTJrg0457S2tjZ2TdKh6wAAAADqL4Suzs31NBqHIDaiXq+nXq83dv2w4wEAAAA0k6OOuBKUEwMAAABABYZ7YglmYyKIBQAAAIAK0J24GgSxAAAAAFCB4ZzYOZ9IwxDEAgAAAEAFinJicrFREcQCAAAAQAWK7sTEsFHRnTiifr+vra0tdbvZ3ZqmqVZXV8eu9fv9OZ4pAAAAgKo5yokr0dgg9uceuqCvXL4a9Ta/64bzescr7zjymM3NTS0vL0uStre3tbGxMXYNAAAAQLMNg1ei2JgoJwYAAACAyMpjdcjExtXYTOykjCkAAAAAVKUcuBLDxkUmFgAAAAAiKweujlRsVASxAAAAABCZo5y4MgSxAAAAABDZSDkxUWxUBLEAAAAAEJkvFRQTwsbV2MZO87CysqL19XUlSXZtwDmntbW1sWsAAAAAmmsk+UoUGxVBbES9Xk+9Xm/sOgAAAID2oDtxdSgnBgAAAIDIyuXEdCeOiyAWAAAAACJzI42d5nceTUQQCwAAAACRhY7EZqNZWZweQSwAAAAARBbC1o4ZmdjIThXEmtmymX3IzB40swfM7L81s5vN7BNm9lD+8cX5sWZmv2pmD5vZX5rZ95Ru5578+IfM7J7T/qMAAAAAYJ68yz4miZGHjey03Yl/RdLHvfc/YmZnJF0n6W2SPum9f5eZvVXSWyW9RdLdkl6Z//e9kn5d0vea2c2S3i7pLmUXLD5vZh/x3j97ynObuX6/r62tLXW72d2apqlWV1fHrvX7/TmeKQAAAIAqhRLixCTn5nwyDTN1EGtmN0n67yX9hCR5769JumZmb5L0fflh75f0KWVB7JskfcBnxeFbeRb32/NjP+G9fya/3U9IWpP0W9OemyT9/Efv11cfe/40N3HAa19yk97+Q6878pjNzU0tLy9Lkra3t7WxsTF2DQAAAEBzhRLijplSTxQb02nKib9T0lOS/h8z+6KZ/Tszu17St3nvH5ek/ONKfvxLJT1a+vkL+dph6wAAAABQS2GsTmKUE8d2mnLirqTvkfTT3vvPmNmvKCsdPoyNWfNHrB+8AbN7Jd0rSS9/+cuPPLlJGVMAAAAAqEoIaMyGnYoRx2kysRckXfDefyb/+kPKgtpv5WXCyj8+WTr+ZaWfv0PSY0esH+C9f4/3/i7v/V233XbbKU4dAAAAAKpTlBPT2Cm6qYNY7/0Tkh41s7+XL71B0lclfURS6DB8j6QP559/RNJ63qV4VdJzebnxH0p6o5m9OO9k/MZ8DQAAAABqKWRfOwkjdmI7bXfin5b0m3ln4q9L+kllgfEHzezNkh6R9KP5sX8g6QckPSzpSn6svPfPmNk7JH0uP+4XQpMnAAAAAKijYTlxtnvSe198jtM5VRDrvf+SstE4+71hzLFe0k8dcjvvk/S+05wLAAAAACyKcnfi8DUxbBynzcSiZGVlRevr60qSrErbOae1tbWxawAAAACaa9idOPuaiuJ4CGIj6vV66vV6Y9cBAAAAtEcIWpNkWE48fjALTuo03YkBAAAAAGOUGztJZGJjIogFAAAAgMjCntiktCcWcRDEAgAAAEBkIWgNzZwcUWw0BLEAAAAAEJnPC4g7tCSOjiAWAAAAACJzlBNXhu7EEfX7fW1tbanbze7WNE21uro6dk3Sidb7/f6M/zUAAAAAphUaOxXdiWntFE1zg9iPvVV64stxb/P275bufteRh2xubmp5eVmStL29rY2NjbFrhx171DoAAACAegghayevfSUTGw/lxAAAAAAQWZGJNUbsxNbcTOyEjCkAAAAAVGX/iB26E8dDJhYAAAAAIgsha74llnLiiAhiAQAAACAyt6+cmHrieAhiAQAAACCyopyY7sTREcQCAAAAQGQhiO0wJza65jZ2moOVlRWtr68rSbJrA845ra2tjV2TdOJ1AAAAAPVQlBMno1/j9AhiI+r1eur1emPXDzv+JOsAAAAA6oURO/FRTgwAAAAAke0fsUMiNh6CWAAAAACIbNidOPuaxk7xEMQCAAAAQGQhZO0kjNiJjSAWAAAAACLz++bEEsPGQxALAAAAAJG5fXti6U4cD92JI+r3+9ra2lK3m92taZpqdXV17Jqksev9fn8u5w4AAAAgpixoDeXExLDxNDaIffdn360Hn3kw6m2++uZX6y2vf8uRx2xubmp5eVmStL29rY2NjbFrhx0LAAAAoP5C0GpsiY2OcmIAAAAAiCyUEw8zsYSxsTQ2EzspYwoAAAAAVQlBa4c5sdGRiQUAAACAyELMaqGeGNEQxAIAAABAZK4YsTP6NU6PIBYAAAAAYjuwJ3aO59IwBLEAAAAAENn+cmJi2Hga29hpHlZWVrS+vq4kya4NOOe0trY2dk3SoesAAAAA6i2UD3fytCHdieMhiI2o1+up1+uNXT/seAAAAADNE2LWDpnY6CgnBgAAAIDIDpQTk4mNhiAWAAAAACIbdiemsVNsBLEAAABYSO/++IP6ud/7yrxPA5hO0Z145EtEwJ5YAAAALKQvX3hOl3bTeZ8GMBUysdUhEwsAAICF5LyXc7zzRz2FoDUJc2LJxUZDJjaifr+vra0tdbvZ3ZqmqVZXV8euSTrRer/fn/G/BgAAYL4GzhfZLKBuwiO3QyY2usYGsU+8853afeDBqLd59jWv1u1ve9uRx2xubmp5eVmStL29rY2NjbFrhx171DoAAECbeJ8FskAdDcuJR7/G6VFODAAAgIU08J7sFWorPHaNTGx0jc3ETsqYAgAAYLE5Tzkx6ix77HZCKhbRkIkFAADAQnI+y8YCdRQq4UMMy0M5HoJYAAAALCTnKCdGfdGduDoEsQAAAFhIlBOjzkLQSnfi+AhiAQAAsJAGztOdGLU1LCe2/Gsey7E0trHTPKysrGh9fV1Jkl0bcM5pbW1t7JqkE68DAAC0ifdkr1BfPn/wWtgTO8dzaRqC2Ih6vZ56vd7Y9cOOP8k6AABAmww8mVjUX+hOzAWZeCgnBgAAwEJiTyzqLDx2hyN2eCzHQhALAACAheScF4lY1JXftyeW6zHxEMQCAABgITlPMxzU1/4glgsy8RDEAgAAYCENHOXEqK/w2A3VxJ7HcjQEsQAAAFhI3ns50leoqfDITUJjp/mdSuPQnTiifr+vra0tdbvZ3ZqmqVZXV8eu9fv9OZ4pAADA4ht49sSixtgTW5nGBrF/+sGv6elHL0e9zVtfdoP+4Y+96shjNjc3tby8LEna3t7WxsbG2DUAAAAcjT2xqLNhd+Lsa08uNhrKiQEAALCQnGNOLOqrKCc2G13AqTU2EzspYwoAAIDF5rynBBO1RXfi6pCJBQAAwEKiOzHqbNidODR24rEcC0EsAAAAFpL3WXMnoI7CI7fYE8tDORqCWAAAACykQV5OzHxN1FL+uDVjxE5sBLEAAABYSKEckxgWdRT2wHaKETs8kGNpbGOneVhZWdH6+rqSJLs24JzT2tra2DUAAAAczbns48B7JbL5ngxwQr4YsUMmNjaC2Ih6vZ56vd7YdQAAAJxMyMTS3Al1FB61xYQdHsfRUE4MAACAhRSaOoWMLFAnbt+IHWLYeAhiAQAAsHB8aUYsmVjU0YFyYh7G0RDEAgAAYOE4X/6cd/+or6KceL6n0SgEsQAAAFg45cCVcmLUUXgM0504PoJYAAAALJxBKRVLJhZ1FB62oZzY8TCOhu7EEfX7fW1tbanbze7WNE21uro6dq3f78/xTAEAABabp5wYNTfsTmz7VnBajQ1i/+Q33qMnv/n1qLe58h3fqX/0E/ceeczm5qaWl5clSdvb29rY2Bi7BgAAgMOVA9cBQSxqKDyGk2LEzhxPpmEoJwYAAMDCKQeuvPlHHe0vJ+ZhHE9jM7GTMqYAAABYXL7UzGnAZkLUGHNi4zt1JtbMOmb2RTP7T/nXrzCzz5jZQ2b222Z2Jl8/m3/9cP79O0u38bP5+l+Z2fef9pwAAABQb+VMLHtiUUfOhXLikInlcRxLjHLin5H0QOnrd0v6Ze/9KyU9K+nN+fqbJT3rvf+7kn45P05m9lpJPy7pdZLWJP2amXUinBcAAABqylFOjJoLD1u6E8d3qiDWzO6Q9IOS/l3+tUn6x5I+lB/yfkk/nH/+pvxr5d9/Q378myRteu93vfffkPSwpNef5rwAAABQb670jp9yYtRRuPgybOzE4ziW02ZiNyT9S0lh18Itkra992n+9QVJL80/f6mkRyUp//5z+fHF+pifAQAAQAs5Ruyg5sLjdjhiB7FM3djJzP5HSU967z9vZt8Xlscc6id876if2f//vFfSvZL08pe//ETnOwsrKytaX19XkmTXBpxzWltbG7sGAACAw43uiZ3jiQBT2l9OzLWYeE7TnfjvS/qfzOwHJJ2TdJOyzOyymXXzbOsdkh7Lj78g6WWSLphZV9KLJD1TWg/KPzPCe/8eSe+RpLvuumvhHga9Xk+9Xm/sOgAAAI6vXE5MJha1FDKx4UsaO0UzdTmx9/5nvfd3eO/vVNaY6Y+99/+zpD+R9CP5YfdI+nD++Ufyr5V//499Vhj+EUk/nncvfoWkV0r67LTnBQAAgPpzdCdGzTmf7YcN1cTOHX08jq+KObFvkbRpZr8o6YuS3puvv1fSvzezh5VlYH9ckrz395vZByV9VVIq6ae894MKzgsAAAA1US4hprET6sjLy8xkCiN2EEuUINZ7/ylJn8o//7rGdBf23u9I+tFDfv6XJP1SjHMBAABA/ZUDVxKxqCPvs1JioztxdDHmxAIAAABRecqJUXNZOfGwhy2P4ngIYgEAALBwyt2JKSdGHXllqdikGBQ73/Npkir2xLZWv9/X1taWut3sbk3TVKurq2PX+v3+HM8UAABgsZWb4BDDopZCOXHxJQ/kWBobxG5/9K917bEXot7mmZdcr+Uf+jtHHrO5uanl5eXsHLa3tbGxMXYNAAAAhyuXELOXEHXkvFdiNuxOzMM4GsqJAQAAsHAc5cSoOe+zpk5Fd2IextE0NhM7KWMKAACAxVUOXIlhUUdeGsnEUk4cD5lYAAAALJxy4Ep3YtSR8350TywP42gIYgEAALBwHCN2UHPeSzLJ8lQsj+J4CGIBAACwcBzlxKg5v6+xE6nYeAhiAQAAsHDKc2IdUWztfPNi3CkhdeQVGjtleBjH09jGTvOwsrKi9fV1JUl2bcA5p7W1tbFrAAAAOJxnT2xt3f/Yc/rBX/20fv+f/wO97iUvmvfpzI0Pc2JDOTGP42gIYiPq9Xrq9Xpj1wEAAHB8dCeur2df2JMkbV/Zm/OZzFcxJzb/modxPNKxVVgAACAASURBVJQTAwAAYOEwJ7a+Uufyj+3+vYVy4sSYExsbQSyAqex+83ld+9vL8z4NAEBDld/wU4ZZL+GiQ9v3MmcP2+Gm2HbfG3ERxAKYyvbvPazn/+ib8z4NAEBDUU5cX+F31/YMetadWEV3Yi7GxEMQC2A6ifGuAgBQmZFyYt7810oIXltfTuxHuxPzMI6HIBbAVCwxnowBAJUpB7FksOolBK9t7yrt5WWyYXdiCoqjoTtxRP1+X1tbW+p2s7s1TVOtrq6OXev3+3M8UyCCE2Ri02d2lFzfVXKWpxwAwPGUX2LaXpZaNyF4bXsm1vns7RKZ2Pga+47yYx/7mJ544omot3n77bfr7rvvPvKYzc1NLS8vS5K2t7e1sbExdg2ovUTHDmKf+rd/oevuul0v+h++o9pzAgA0Bnti6ysd0NhJCuXENuxOPOfzaRLKiQFMxczkj/ni5K6k8lfTis8IANAk5VLUtpel1g2NnTKhfHjY2GmOJ9Mwjc3ETsqYAjiljkmpO9ah3vljB7wAAEj7glheQ2olNOJqfRDrpaSUMuRiTDxkYgFM5wSZWHlPLRgA4ERc6TopLyH1EvbCtr2rtPehsdO8z6R5CGIBTMUSO9bmDu+95EQmFgBwIgPKiWvLUU4sKXublI3YyffE8jiOhiAWwHQSkwbHiWLzjy1/IQMAnIwniK2tlCBWUuhObOyJrQBBLICpWHLMK4rhBazlL2QAgJMZlMuJeQ2plUFeC972IDYrJxbdiSvQ2MZO87CysqL19XUl+Q5u55zW1tbGrgG1d8w5saGMmKuPAICTKGdfj1P4g8URLkC0PoiVJObEVoIgNqJer6derzd2HWicYwaxZGIBANMoB7HsJayXIhPb8t+b936knJiy+HgoJwYwlWPPiQ2ZWIJYAMAJlEuIefNfL+yJzXifZWGNcuLoCGIBTCcx6RhjYj2ZWADAFMolxIPjjSXHgqA7ccZ7jY7X4WJMNASxAKZiyTEzsXQnBgBMge7E9UUmNuPycmIpu/bf7nsjLoJYANNJdKwrip5yYgDAFMoBEHti6yX87tp+8aH8rzczErEREcQCmM5JGzvxxA0AOAFHOXFthSA2bfkF7KycOMvEmgjqY6I7cUT9fl9bW1vqdrO7NU1Tra6ujl3r9/tzPFPg9E7c2In5CACAE3CUE9dWCF7bPt83606cfW6UE0fV2CD2a197hy5dfiDqbd54w2v0qlf93JHHbG5uanl5WZK0vb2tjY2NsWtA7Z1wTiw1NACAk6A7cX2Ric14DRs7mSgnjolyYgDTSUz+OOVddCcGAEwhzBjNrpnyGlIn4XfX9sZO3nuZiihWnlxsNI3NxE7KmAI4HUvseNnVkIht+QsZAOBkwstGN0m4DlozgwGNnaTsMRzKiRMT9cQRkYkFMJ1ExysnHpCJBQCcnHPZfsIkYW9l3YRMLOXEKuqJTUYMGxFBLICpWD7wbGKGNVyFpbMkAOAEwozNxKz1Gb26GdDYSVIoJ86YcX/ERBALYDqhU8GENxbMiQUATGPgvZIkBLHzPhucRMjAsid2WE5MNXFcBLEAptPJn5UnZVjpTgwAmEIIABIjGKobRxArKWvkVMyJNboTx9TYxk7zsLKyovX1dSVJdm3AOae1tbWxa0DdhSdl74alMmORiQUATME5r46ZksTkefdfK6nLrnAPWv57O5iJbff9ERNBbES9Xk+9Xm/sOtA4yXHLifNPBjxxAwCOb1DaE9v2YKhumBObcaURO3bMoQ44HsqJAUwnf/bwk4JTyokBAFPwXuyJrSkaO2W8l4oxsXZk3RpOiCAWwFTsuJlYTzkxAODkBmHEjoly4pqhsVPGq1RObMzNjYkgFsB0wrPypBeoYk5stacDAGgW5706iamTWOuDoboZEMRKCiN2wpxYitJiIogFMJWQifWTglMysQCAKTifdXalnLh+iiC25VGb98OJhGbW6MZOA+f1yQe+NbOqCYJYANOx42Vii+CVdyAAgBNwTuqYUYZZQ2RiM1k5cfZ+KWl4Y6dPP/y03vz++/TgE5dm8v+jO3FE/X5fW1tb6nazuzVNU62uro5dkzR2vd/vz+XcgRPrHLOcOGRqW/5CBgA4maw7sdRJrPUNguqGPbGZrJogfGUNzsNKV6+lkqQr1wYz+f81Noj9uYcu6CuXr0a9ze+64bze8co7jjxmc3NTy8vLkqTt7W1tbGyMXTvsWKAuwpPyxDLhMCe2yZcfAQDROe/pTlxTIXPe9iC2/NbHGt6gbNYXLignBjCdY8+JpZwYAHByzmVzYs3YW1k36YAgVhotJ256Y6fwO08Hs+nk2dhM7KSMKYDTKRo7HXdOrMu79DEnDQBwDM5npcQds0ZnsJqIxk4ZXyontobviQ2Z2JRMLICFVtQTH33YyBuPBj95AwDiGuQBQGImx5i2WgnBa9v3MnsvhUv3pmZ3Jw4Z2HRGf6wEsQCmc+zGTn785wAAHMF7yonrajDjrNyi8vKt6U5cZGInVehFQhALYCrHb+w0/JRZsWiTa397WXvfemHepwHU1sB5dczUSSgnrhtG7GSc2z8ntrmGmViCWACL7KSNnY5xLNAk2x9+WM99/G/mfRpAbTmvYTkxLx+1EoLXts/3zf71w14gTb4/Zr0ntrGNneZhZWVF6+vrSpLs2oBzTmtra2PXJB26DtTCSRs7SdKMSkyAReCvOfkzbOQDpuWcVycxJYm1PqNXN2FfZOvLifNZx1KekW3w3THMvtOduHZ6vZ56vd7Y9cOOB+rKpsjENvgCJHCAd05+RqMGgCZy+Z7YxJqdwWqi8NRHYyeNdiee7+lUKlyw2GNPLICFFoLYSe/Ry288Wv5ihpZxovoAOIWBl5LE8nJi/pbqZEAmVlLW2MkU5sQ2e2/3rGcDE8QCmEoxJ3bSk9WAIBbt5AeOZmbAKYRSzA4jdmonBK9kYqV852DWnXi+p1OpooR8RhVIBLEAphP6FEx4gRoZE9vyFzO0zMCTiQVOIXQnNsqJaycEr20fjeR8KRNr1uhtVbNu7EQQC2A6CXNigaN45yc3PgNwqOGeWMqJ6yZ1sy0tXVReKi76m5p9MaYYscOeWACL7NjlxOXGTi1/MUO7+IHnwg1wCs5lpZidhBE7dcOc2Iz32YgoSVLjy4kZsVNb/X5fW1tb6nazuzVNU62uro5dkzR2vd/vz+XcgRObak5shecDLBrn6U4MnILzXl1LKCeuoVBGTBDri91XJjX6fRAjdiL5+Y/er68+9nzU23ztS27S23/odUces7m5qeXlZUnS9va2NjY2xq4ddixQG0UmdsJxlBOjpTx7YoFTGfhsTmwnsdY3CKoT53xxfbv1QayGI3YSM/kGR7FhtA4jdgAsNDvmnlhPOTHaytGdGDgN57NmONme2HmfDY6rXE5KYydflBObTSxeq7WQgZ3VhYvGZmInZUwBnFK4BDbpyar8bd6FoCW895ITjZ2AU/Deq2NZ4U/bM3p1Ui79bvvvzXuVyomb3aAsNHTam1E5MZlYAFOhsRNwhPBYJ4gFpjZwdCeuo5CJPdNNCGK9ht2JG56JLTpSU04MYKHZycuJycSiLUIG1s/oijTQRM5LSZIFsU1+8980IXA9203kfF6Z0lK+VE4sNbqvk9L89Y45sQAW21RzYqs7HWChhMc9F26AqTnnlVg2ZqfteyvrpBzElr9uo1IiVtbwizGhnDilnBjAIrMpuhNTToy2KPbCunZnIabx/B99U1fvf3rep4EF4PLuxJQT10sIYs508iC2xb8778vdiaUm52LDxYp0RuXEUzd2MrOXSfqApNuV5Vfe473/FTO7WdJvS7pT0t9I+jHv/bNmZpJ+RdIPSLoi6Se891/Ib+seSf9HftO/6L1//7TnNU8rKytaX19XkmR/tM45ra2tjV2TdOg6UAvhEtiJ5sQ298kbGFF+ER94qWuHH4sRl//8cZ3/rlt0/nW3zvtUMGcD74vuxLx81EdIxJ3pJiNft1GbuhPvhSC2Bt2JU0n/wnv/BTO7UdLnzewTkn5C0ie99+8ys7dKequkt0i6W9Ir8/++V9KvS/rePOh9u6S7lF2e+LyZfcR7/+wpzm0uer2eer3e2PXDjgfq6tiNncoxLJlYtMT+0VKEsMdn3UQ+5bkC2Rv+jmVzYttcklo3IRO7lGdis687czyj+SnPiW16d+JZj9iZupzYe/94yKR67y9JekDSSyW9SVLIpL5f0g/nn79J0gd8ZkvSspl9u6Tvl/QJ7/0zeeD6CUmkJIFFd9zGTuWMFG9C0BaDUuqBDsUnYkuJfNri1A0Kg3xPrJka/ea/aYo9sUtkYrOHbSkTO9ezqdZeGLEzqNGeWDO7U9J/Lekzkr7Ne/+4lAW6klbyw14q6dHSj13I1w5bB7DIjtvYyRPEon1GMrEzekFvCusaQSwk5aWYdCeunRDEnhnJxLZT1p04+9zU7HLi8Htf+ExsYGY3SPodSf+b9/75ow4ds+aPWB/3/7rXzO4zs/ueeuqpk58sgGhONCe2e8wmUEBTUIEwvW4iEcRCoTuxqWOUE9dJ+F0t0dhppJxYZo3OxKb5Bdu9OsyJNbMlZQHsb3rvfzdf/lZeJqz845P5+gVJLyv9+B2SHjti/QDv/Xu893d57++67bbbTnPqAGJIJtfGeOdl+QsZb+bRFuUyek858YlYN5HfI4hF9pLRMVOSUE5cJ2lRTpztg21xIlbee1mer0us2d3q0yITu+DlxHm34fdKesB7/69L3/qIpHvyz++R9OHS+rplViU9l5cb/6GkN5rZi83sxZLemK8BWHTJ8TKxlncobHQdDVBW/rsgiD0R6yYE/pCUZfCSJJuvSRBbH5QTDzmvkXLiJitG7NSgO/Hfl/S/SPqymX0pX3ubpHdJ+qCZvVnSI5J+NP/eHygbr/OwshE7PylJ3vtnzOwdkj6XH/cL3vtnTnFec9Pv97W1taVuN7tb0zTV6urq2DVJY9f7/f5czh2YhiU2ubGTy/a4SWSk0B77uxPj+KybyF1N530aWAA+H0+SzYmd99nguIrGTozYyTKxxYidZl+MCQ2dFn5OrPf+0zr8osIbxhzvJf3UIbf1Pknvm/ZcxvrYW6Unvhz1JnX7d0t3v+vIQzY3N7W8vCxJ2t7e1sbGxti1w44FasUmB7HZnlgysWiZUjMnLt6cjHWNcmJICt2JTQndiWslZOLCnFj2xGaf09gprijdiQG0k3XsGHNih3tiyUihLUYCV7oTn0g2J5b7DMNSzIQ5sbUSLjgsdbLobVZ7JBeR9yr2xJo1O4gtRuzM6Pd9mnLixTYhYwoggmNkYr3zRTmx2vs6hpahnPgU6E6MnHOM2KmjUE56tps1dmrzdbysnDj73MzkG9yfmEwsgPo4RnfikcZOvJlHW4xkYnncn4QtkYlFxnnKieso/K5COXGbGzt5qTVzYsPveVYjdpqbiQVQOUuOsd/PeYlyYrRNORNLEHsi1iGIRWbgvTqJyWx22R2cXtgTG+bEtjiGlRtp7NTs+6I2I3YAIB96duQh5e7EZGLRFiOBK4/7EyETi8D57I0/5cT1EoKYszR2yvfEZkwNLyfOX/dm1Z2YIBbA9JJjNHZiTixaaGRPbJs3hE3BuonkyGAj2xPbycuJ2xwI1U14yiu6Ezc5/ThB1p24JY2d8t9zHebEYp+VlRWtr68rSUL5hNPa2trYNUmHrgN1caw5sX4YxFJOjNYoB64EYydSPF8MnKzTmfPZYJ7YE1tPBzKx7Y1h9zV2mtxGpM5m3diJIDaiXq+nXq83dv2w44FaO86c2IGXdehOjHahO/Ep5NsP/J6TzhDEtpX3Phuxk1jREMeX9hdicR2YE9vi58ByOXFWFt/M+8J7PxyxM6OrFpQTA5iaJSY/6bnK09gJLUR34qkV2w/YF9tq4b1+ku+JldheXhchaD3TIYjNuhPbyNdNVP4VM2IHwOJLdLw5sR3LLkW2+IUM7VLez8nezpMpyokJYlst7IHtmIXroJQU18Rgfya2xb83t29ObFPfBpXHKJGJBbD4jtGdWE7hUjpBLNpjpJz44Av63hMv6PJnH5/lGdWGLRHEYhiwJokVJcQEsfVwsJy4vX/Lo92J1djOTuWOxGRiASw8O0Z3Yu+yZ3BLmrsXBNhv5O9iTCb2hc89oec++vUZnlF9WNh+kPJ80WbDcmJTJ8mD2PbGQrXi9s2JbWtjp/CeZ6Q78TxPqEJpqYR8jyAWwMI7TmMn57MuxtmMhNmcFzBv5XLiMX8jPnXZf1zYOYBMLKRhNie8fEhkYusiBDRnW97YKTxci3JiNTYRqzS/UnF2KaE7cR31+31tbW2p283u1jRNtbq6Onat3+/P8UyBSI45J1aJNfsSJLCPnzBix6c++3sY+KIbLzJW7k6M1goBa8jCSu3eW1knB/bEtjWIzT9aXlCcmMk39I1Q+B2fW+ro0k46k07ijQ1i3/3Zd+vBZx6MepuvvvnVesvr33LkMZubm1peXpYkbW9va2NjY+wa0ASWSD49+hjvsyDWOnQnRotMGLETsow+dcNuvMiU5sSivULpsJkVewondsOP5JMPfEveS//ktd82m/9hwwz2Z2JbevEhVNokRWOn5mZi94ogNvudp85rqUMQC2BRJTb5XUUoJz5O6THQED5kWFN/SCbWjXzEUBHUk4lttSITawfXqvZv/8vXNfCeIHZKwxE72Zxn19LX/vDPHiYkm9udeJC/zp3rZr/zgfNaqnjMd2OD2EkZUwCnZ4kd+eLkvc+6E9vxmkABjeG8rJvIp4PxGcUiiOVvYj9G7EAaZu+SOZQT76QD7dHDYWr7uxOnLX3tD6XDI42dGpqKDSN2zuaZ2L2B07mKo9jGBrEAZmDS2JxwFZIRO2gZP8jmI/vO+Md9EaARqB1AEAupNGKntK9uVpnY3T2n3XQwk/9XE4XfUwhi25qJHdfYqanChYpyJrZqBLEApjepRDh8jyAWbZM3NLPEstLifUIGlkDtoGF3Yp4v2izsiS0HsbNKYu2mA13dI4idVpgZ2vpMbAhiy42dGnpXhN95yL7OopKBIBbA1KxjR26J9aUgNpsTO5vzAubND7wsSeQ7jj2xJ0QmFlK5O/FwbVZdbnf2nK5cI4idVij7Do19WtvYqSgnVvGxqd2JQzlxaOxEJhbAYjNNKCfOn8ATy6ZSt/RqLFpo4KSO5Rd6CGJPohixw33TauFNsM2jnDgdaIdM7NQGzqmbmDr5767t5cTl7sRNvSuK2cBFJrb652+C2IhWVla0vr6uJAl7AJzW1tbGrgFNkGVXj3hGHpSewY3GTmgP7/I9sUlCJvak8tQbc2LbLby0dMyK3JWb0UNiN3XaG3jtDZyWOozAOqnUeSWJFTN+21pOHC66hHJi04T3TDUWyonPznA2MEFsRL1eT71eb+w60EiJjX2DHpSbGhh7YtEmgzAf2ehOfEKWWDZXpSFzYh995op+8zOP6F9+/98b6bSLow27Ew/XZpeJzR57V/cGBLFTcM5nmdik5ZnY/GNRTGBqaDFxuZy4k39d/b+Uv0wA00smTO52xaV0GjuhVXyYjzyhnJjuxONZN2lMJvaPH3xS/+a//LUeeebKvE+lVsrdiUNzp1kEsXsDV2SRrrIvdiqp8+qUgti27okN2cluEjKxamwUO9jXnTidQdkEQSyAqU2c/Vqa9M2cWLSJH/hsT+wh1Qp+j+7ER8lm7Dbjvgldbp++vDvnM6mXkL1LzIp9sbMIYndLjzuaO01nkAex4eLDrBpyLZrwt3/+TBbYJaXS+KYZdidORr6uEuXEAKaXmHSM7sTFnNimPnsD+zkv6yTyiRs/YmfAntijZEFsM54wQjaPIPZkikKeUmf7WcRCu6WGTmRipzPIy4lDBrKtQWxoDhZKbM3U3D2xIRNLOTGAWjAdLxObdyce92YeaCJf2hO7/523d77IzhLEjmdLzcnE7qTZG9mnLl+b85nUy6DIxA67u84iE7tTetxd3UsPfP/+x57Tr/zRQ5WfR50N9pUTt7WxU7gIUgSxau6uqnSwf8QO5cQAFtikZk3lTKzZhP2zQJPk3YnVSQ42dip93ZRsY2zWteYEsfkb2acukYk9iaKzq1nREGsWGb3RTOzBx+Dv/+Xj+uU/+tpMRojUVeq8OnkZeGLtbewUMrHni0ysNXhO7Ggmdo9y4nrp9/va2tpSt5vdrWmaanV1deyapLHr/X5/LucOTGVSs6bwrURZc6eGvCkFJvEDJzvbGbsnttywqCmBWnTdpDHPF+yJnU4IYssjdmZxHXR0T+zBTGzYJ3vl2kAvOk8uaBznvDqd7MJDJ7HWNnbavyfW1Nxr+aGREyN2Injine/U7gMPRr3Ns695tW5/29uOPGZzc1PLy8uSpO3tbW1sbIxdO+xYoFYmzYktlxMzJxZtMgiZ2IOP+3L2tSkdeGNrUnfinfzf8TSZ2BMpXj6S4Rv/Wbwx3ilnYvcO7okN39/ZG+hF55cqP586CplYKWtm1N49sdnffsjEqsEFacPGTuyJBVADxy4nzrsTN3YzCLBPecTOgUxsOcNISeJY1k0as4c+BEJPkYk9keGe2GE58ay7E49r7FTOxGK8sCdWysbLtDWIvVo0dsrCraQYGNs84Xd8NozYmcFrW2MzsZMypgAiMEku67Zn456cRxo7Hd3JGGgUNxyxs38/WDmIbUq2MTbrJnJXD5Zy1tEO5cRT8aU5seEvaCbdiSeM2AmBCZ2LD5d1J84DtxYHsTtjGjs1tTvxXrEnNh+xQyYWwCKz0DLykOcqXwpiLZnQyRhoED8ImdjkQLZ1JIhtSLYxNutaYwL8Ioi9dK2xb2CrEAKfbN5otjaT7sQTyolD8DquczEyqfNF9rzT5iA23d/YqbkFaYOiO3HIxBLEAlhkw3cW479/YE5sQ5+9gf0GXuokso4dCFTJxE6WzYltxn1TZO72BnqB7N2xhZcVMxX7K2fR5XZSOfEwE9uMx2cVnPfFjNhumxs7HcjENr87cWjslDJiB0Bsz//xI9p5+Nk4N1ZkYg8LYsNx2bFkYhfX8596VDsPRXpcQN65rLHTuL3gKd2JJ2pSd+JSINSm5k7v/7O/0ce/8vjUP1/uThy2q8yknHhCJna4J5ZM7GHS0p7YxEyDllacDPfEDjOxTY3n94/YIRMLILpLn3pUV//i6Si3FcqJDwtOD8yJJYhdWJc+dUFXvvTUvE+jOZzPyujHZmJLXzckUIvNlpqTid3Zc7rtxrOS2rUv9r2f/ob+430Xpv75EMQmiRUB0UzKifPHXWKH7InNg9dxAS4yA+dGGzs1NXKbYGfP6Uw3Ke4LK+3vbpp0XzkxI3ZqZmVlRevr60ryzezOOa2trY1dk3ToOlAVP3Dy15zcbqQryEU58WH/w/2NnZr69F1v3nv53VSeUsdofD5ix4+pQCiCMyMTexjrNCmIHeg7brlOT13abVUQe3k31eVTvNaUuxOHEsyZdCfOg9MXnV8qAtYyGjtNVu5OnIxpbtcWO3sDnesO84XtyMTOrrETQWxEvV5PvV5v7PphxwOzFLp9+kh7eUJD4kmZ2Kyxk8k34z1p4/g9J3nJ7fKmLJrBMBN72IgdO9ttTKAWW5MysVf3BnrZzdfpC49s66mWlBN773VpZ08vnKLktnwN1Cl7sZlFdifsiX3xdWcmlBPzfHmYgfNa6mTBTCexmQQ0i+jqtYHOn+kUXze5O/HAeSWm4vfOntgpNPXBcVxt//fjaCGIjZaJ7RyzsZNZ9mzT0heyRefz4NUTxEbjXZaJtc7BeachOEvOd0ZLi1GwbpKN76r5XjrvvXb2BnrJ8nmZSU9dvjbvU5qJ3dRpb+D1wimeU4o9saVy4lm8xQmZ2JvOL40NVHdKjbowXnlPbKfN5cTpoOhMLOWZ2DmeT5X2BtlYpaW8wpQ9sSd07tw5Xbx4sbWBnPdeFy9e1Llz5+Z9KlhQfidysGLHC2KLcuKW/m0uOreTZ+gJYqPw3heZ2CyNNH7ETnKOTOxhLC/B84N63z/XBk7OSzec7erm6860ppz4Uv6cEquceJYjdnZTp7PdRNed6YyM25GkvUEWnEuUEx/FlYNYa2858dVrg2KPqJR3J27o+6CBc+p2TJ08uTGLTGyjyonvuOMOXbhwQU891d7mJOfOndMdd9wx79PAgopeTlxcHT+snDj/JJQT1zyr0lQheHW8KYsjjAbpJFLHH3zc59lXO9eRu0KH07G6+XPLnpNK5Xh1s3Nt2Ozk1hvOtqacOASvV04RxA6vgVqxNqty4nNLHV13pqPHn9sb+V45M0s58eFS54uxSK0uJ97bF8Q2PBPbSawYrcSe2BNaWlrSK17xinmfBrCw3NXsBTl6Y6fDgtOiO7HIxC4wRzlxXOHvoWPZf4c0dkrOdeWeb0d56UmFTGzduzfvpNnf1Pmljm678WyLMrHZa80L1wZyzitJbMJPHDTsTizJzW7Ezs7eQGe7ic4tdQ6UDO9MGL+DzGBfOXFbM7E7e6PlxIlZY98GDVw2G7gIYiknBhCTuxqCldiZ2EMO2NedmDmxi6koM29ZZiHd3tHF33pQPvKbUZ+XUVlegSA/2vzM71FOPElRTlzz+yeUnJ4/k+jWG860JxO7M7xQemXKv6/ynNh8m91MSjF3U6ezS1k58f6S4XL2dVzn4rr6hY9+VZ/9xjPRbm/gvLodMrE7e26ksZPU3N41qXPqdobjhGbxOyeIBVpkWE48iBNQhmeQw7oTD0pzYrMWk1hAITPv91yrSr53H9rW1b94SnvfuhL3hgelizd5p8ZytYIfOMkkO5PQ2OkQttSQIDYP4M51O3rx9Wf03JW9CT/RDM+XgtgXpqz8GZQaA4aS4lk0CNpNBzrX7ej8UudAyXA5qG1KJvZa6vS+/+8b+s/3PxHtNrNOtaVMbEMDt0mycuKWjNgZZJlYsywbCbUuCgAAIABJREFUm86gnwFBLNAiIYiVhtmgUxl22xj//fBsbZb1lm/p1dhFVy4jjp2VXGSDvFNs7H2pxcWbjg2rFUpNLnzqZN1E1m3OGJnYrBOC2Ho/ZxRB7JmObjq3pEu76Uz2dc5buaHTtM2dwstHJxkGsbMpJ84ysefPdA8Eqlf3ShnmhlSuhNLv53fiXWBJ89JSKcukt+ExP87Yxk5zPJ8q7c++z+J3ThALtIgvXR2Psf/RLLxBnzAnduTNfFOfwuurPB+2TbNi3aV8j/jVyNmx0uO+GENVznCnXuomjZqFGltTMrFhD+X5pY5uOr8kabTUtqkulQKi02ZiQ5NvSTPZW7mbDnS2mzV2upa6kTfjV/NGXUsdO9C5uK5C1vz5q/Eel9me2OxvOGlxOfHuuBE7DU3F7rlsxI6UzYqlnBhAVOVMbJROtJMysfn7z2xO7IRjMTdhT6w0enFj+3d+R19/0w+PZBGbpPJMbJLI8iC2XKZdzsRq4LmwM4aVuxPXWAh0zi11dNO5rJdmzIzXorq0c/pMbNHYyYZzYmcyYmfP6dxSUgQf5WzslXwf7C3Xn21MJvb5q/EzsVkQm33ebXFjp4OZ2OZ2Jx44N9LMi3JiAFGNlBPHyLhNyq4WMxKGxzZ2Q0iNlbtVlx8X7spV7f7VX2mwvT2P06rcIGRiYwex5UzsmL8Rv+eyIC104K35LNRKNGRObMjclTOxz8XO/C+gcuD6wpSvNcPuxDMuJ84zsaEhz5VSA6cQ0N58/ZnGzIkNweuliBUCAz/MxHYSm8le5kXjvdfV/d2Jk+Z2J94bDEvIuzPKvhPEAi3irqbDN4cxyomLwHT898Mb96KxkygnXkQjgWvpjVn31lskSenTT8/8nGbBhUxsxDI6SUVQaokVmdj9jZ2KTKzqv++zCsWInYZkYs8vZXtipbZkYk9fTjy8FmSyWZYT7zmd7ZYysSMdibPPb7nhTGMaO4Uy4tiZ2BDQJC3dE7s38HJeI92Js9Ygzbwvyntiux1jxA6AuNzVVN0Xnck+j1JOnH+cMCd2dFPT6f+3iMsdUk7cuSULYgcXL878nGZhmImNG1SUGzuFmrpyRjHLxCaNGSNThabcN0Vjp6VEN53Py4ljXzRZQJd2Ut1wNvv3TltOXN4TO9Ny4jQLYq87M66ceJiJbUw5cWjsFLFCIB0MS0u7M2rys2jC4+ZstxRqWXPLifcGrtgT203YEwsgMr+TqvOis9nnMcqJQ2Onw95YlObEhivpZGIXj99NlVyfZYn8SCb2NklS+nTzglifuqLRWfRMbOnijY3ZC+4HPg9irTgXjGpKELuzrzux1JZMbKpvuyl7rZk2E+srKif+tU89rP/wmUcO/f7OXraP8VxRTnxwrM7N1zcpExv2xKbRmg45P7zwkLQ0iC2qMM6Mdic+aRT7vk9/Q+//s7851rH/6mMP6A++/PjJ/geRlLPv3Y4pnUEvDYJYoCW883JXU3WW4wWxRankhO7ElpS6tLbwxWzRud2BOjedKT4PmlxOPLg8DCRiB7HF475j4xs77bmsO3FDArUqJOc6OvfaW4qLbnU1rjtxzIzXorq8m2rlxnOSYnQnHl4EjbG38j/ed0G/84ULh36/yMTm5cQ7+8qJE5OWz5850Lm4rsJFlYHz0bLLqSMTW/7bD8wkf8Io9kOfv6DfPeLxGnjv9YE/++bcgth034gdMrGonWd/72FdffCZeZ8GxvDXBpJXEcRGKScOV8evpnr6N+5X+szO6Pdd6TgjiF1UvhTEli9uJDfeKFta0uDiYgaxz338G3rqvV+e6mfDflh1k+iNnYry+nIZ/ciInayxUxHE1nzfZxWS65Z06/prde6VL573qZzK1b2BOolpqZPoxrNdmQ1HmjTZpZ093XS+q+vPdHR56sZO2cfsGuiEqp8TePryrh7fvnro93fTgc4ulRs7jZYTX3emW5Qal5s+1VW5oVOsKoGsO3EpE9vQfaBHuVrqTB4kdvLelhdf2NXT4fXqCJd2U13dG+jiMY6tQloqJ15KEg3YE4s68QOnF7Ye1879zSs9bIKQbYpZThxKJa89ckk7Dz6j3W88N/L9onQ4GR7b1BlpdeZ2BkpuPJiJNTN1br11YcuJ/TWna9+8NNVjanApe6Ffuu189Dmx5T2xRSa2XE6cj9gZdifmb6Kprl5zRSYmSUw3nO22IxO7k+rGc0u6/mz3FI2dDpYTnzajt5sOdGkn1bcu7Y69Le+9dvaczh2yJ/bqvlLjJpQUlx+PsfZrl0tLOy1t7BSagI1kYmUnysN673Xx8jVdfGF34uvck89nSYSLL+ye+FxjKF+4yDKxlBPPlfdeFzcf1M5fN3O8RGyhPC+8OcRiKYLY65ey7FPEObHps9mT54HfvfOS7ZsTyxv2heN3B0rOdWVnkpE9sZLUvfXWhS0n7tx8Tv7aYKpMqsufr7or18ldibcXLLvx0FY1GZbRlxs7pU62lEyVifUDr4u/+YB2H3k+2ulO4nYHevz//JyufPHJkfUXPv8tPffxv5nZedTRTjo6J/Kmc0ut2RN7w9mubjjb1QtTZivLc2KTorHT6c7rmRey16iB83ry0s6B71/L/06zTGzWmGq0O3Gq6850ilLjJozZeT5yJta5rCtvuPBQt3Lif/HBv9Cf/NWTkw/c58/++mn9zOYXi9eScZlYs5M1J3v+aqrUZRdWJpV6f+v5LHidVSb2fZ/+hn7tUw8XX+8NvJbK3YkpJ54vd2lPV7/0lK5+ZTHfwC0alwcwBLGLKQSxdr6r5GwSdU7sIC8jduOC2PyYYSb29P9bxOOdl782kJ3tyM50DjwuurfconRBuxN3b8723A32l7Efw+DyMBOrgY9a0js6Wip0J97X2KlTaux0glmog+d2dfXLT2tnhts2Bs/vanBx50AG4cpfPKXLf/4Y1RVH2Lk20Pkzw7daN51fanx3Yue8Ll9LddO57qkyseHPomNWGjN+usda+Q3+Y9sHnzd28/3p5RE7++fEni+VGjclE3vj2W7x+WmF0uFuqZx4FqORYnhhN9XvfOGCPvi5R0/8sx//yhP68JceKy6U7OavKeeWhn//ppO9B3q6lFWdFJx+K8/EPnPl2kwuGnzwvkf1W58dNkgrZ2K7CSN25q7ILj07n9R83YTg9UAgg4Xg8zdOybmu7Gw3Ujlx9jHN/0b2X8Dw3g+7s4Znm5q8mLVFyLwm5zpKznZGyoklqXPrLRosaCY2BLEH9mIfg7u0JzvbUSfvoBp1X+zw3XeRiR0pJ96bPhM7yK+2D56f3fPs4Lns/xX2TQ/Xd+V3B3KXmp9ZnNbVvYHOdcuZ2G7jM7EvXEvlvfJy4o5emHpPbH4xyBStnPjpy8P3c48/9/+3d97xdZ31/X8/dy/tYVuy5T1jxyZxBglJDBlkhwQCKaNp2kIZhZaWQtsftNAGSEgYZSaQQcJeIXsv2xmOV7x3ZMuWbO15dfe9z++PM3SvdDUt2Vb4vl8vvywdnfGcc55zzvP5rmdgXqxVjMfrdmaFE/c9n5FEGr8nf77sZKU7lqS6xG//fLxY98jp7AsnPhFeufGg0RSCG+s6Rm0wOdIeAeBYl7GPaJ7qxHZtkBGSLVxbhwkTtjyxWkNHZGK/D1pr6jui1HdE7WcmmcngcmZPsSPhxCcVy7o/lgHSXyLWQCYdTso0KqcglifW4Xfh8DgGiJUxYXlXzXCkdP/BbLrPE9s3T6z0jVMJqx84vC7DE9s/nLisnFR7O/oEfJBGi7PEFLEdY/PEOgs8OAKGB2I854q1c2IdfTmx2WH0OpUBp0K5R1+d2BKUJ1TEmoOjASLWbEOyJXLC2jLZiCXTOYPYQr87p5DO2xHr/EI+I5x4rPPEWt47p0NlzRN7fG3L9cQOFLGW98zrcuB1OVDKCCG2iCbSb79w4miK6SUB++fjxRaxpmBzOtUJmd93PGgyBWhLT5z6jsGLf+XjiLm+1a/y58QajFQgt4VH7onNDo+f6JDizkiScNwwVtW1Ge//AVPsiCf25GKJ13RHTMKlRoDthTOnchFOLWwRGzA9sePw8VX9rIoDvPAa2/JorXsiDRzJlgjNd28j2dh7wo453kQ2buTAJZcS2717QvZvGSCUz4ny5gknLi+HdJp056lXG8DhdeIIuscWTtyTxBFyo/ymiB3Pd5adE6vyG2+snFjnwFDj4bA9sV0nLkLI9sRmTXejk2k7uiPVOrrB3l8SViEgi0Kf+5Qv7PTFP249rmk6LNFaYIUTjzkn1vg/e4qd4xVDVtEbt1MNG06slMLvdg6YJzYnnPjtIGJjSaZbnthx6JuWeHE6Town9isP7+CRLQ3jsi/LEwuwqa5jxNtprQd4YmOpgSLWoUaXVtXa2zemyha0+Wjuzha8E/t9ONLRZ7isbQkDxn3PLewkIvakYolYnczYRUCEwckOJZWQ4lOPTCxlFFnyOI0CPuPoibUYEE6c0XbI8Yn2xKa747Teu4PEwS5iB049ATZSHMEgyfp6EnV1E7J/yxOrvGY48YDCTqf2XLHOUt/YwoltT6wxd+d4hhPn88Tqfp5Y5XKAlSs1mnDik+SJVT4njiyPotUOgFSLiNjBiCYzuSLWf2qHE8eSaX6/sZ7fjiEn0KLHPL+Q9zhzYu3CTn2D/+PNrWwLJ/C6HMwsCw4ZTmzds8oCry1KwBCtfk9fqHFkkufEJtNGwaCyoAe/2zkuffOAKWosYeycwMJOqXSG36w/zGNbx2duVEvE+t3OUYnYlp64bQA52pXrifX2K+wEjLhCcY4ntnf4nNiKAsPQ2DrMusfL4fYsEdtqOAlSmQxua4odp+OE5OW6JvwIk5h0R8zMwjYErbPAM+w2f8lkC9d0TwL31OBJbI3Qn0w0hfK5UA6Fw+sk2TUOL7ksEav8LnQ0RSaR7hvsZgaGE58oT2zrg7sMYeJykGqbvINs94waABKHxz6oHAods8KJTU9sP0HoLDNEbPokFnfKxFI0ff9N+/fSmxbirSkEwFXiJdEQHvU+0z1JvPPcfeHE4+kdy65ObP5o9Xud1qAxCjvZAnf0ObEDnrUJJN2VsHOH+7cDxBM7FLFEmqlZ167Q5yYcT5HJaLvi7qmENTh9s65jzG20wokLfO7jCifWWptTjCu7yPfxfj5awwnKQ16qiv054tQi2xMLMKcixFstfe8XyxNridzYJPfEWveq0O82DCzjEJGy2RR/Z8w05nh2TmBhp2NdMVIZzeH28Ym2auyKUehzsXxG8ahEbLZn8pjp4bcMIvnCiTNa42T4Z6stnKAk4CaV1jn53Plo6omxeFohLT0tE++JbTfe+YU+F7UtxrVPZ3RfHrRDkRzFd22siCd2CFLtMdxVIcAUtMKQpMNJnMXGx3qAR26Yzizh2hNPJprCYYZO5st9HA6d0QPuk8oa4HimG89KdtSCzlOdeFSTpI2RdDhBsj5MwXtm4K7wjync9FTBGQriLCsjcXh4T6zO6FGFpkJWTqzPlb86cXkFwMmdK9ah8M4stP85fH32V1epn3RHfFTGEZ3KoGMpnCGP/UxMiCfWqfr6vfkOtPJfldvRlxM7qsJOibw/TyTp7jjOov5FnczqzlODpCQndlBiqXTOILbQ70Zr6BmjsJtorPy2nniK/c2jNw5BljDyuQh6XMSSGVJjGNCmM9rOqzzecGLr29XWG6cs5KGqyDdIOLHpPTOLcc0pD3KordcWYRErJ9acficyxlDpUwUrfLjQ7xq36Z821rVTUxqgssCoWTCRoaVWfz3cHhkXodzYFWNqkY8zakrY09g9YgOMJerKQ17bwx9LZnA6lD3tDGR5YkfYVKO/eikLeYbMc9Va09QdZ0FlCKdDTXhO7JGOCCUBN6dVFVHbarwnkmmN2/zeuZ0nZlolEbGDoNMZ0l1xvHOLAEi1Td5B8Iki3ZPAPc3wvmZXq+xZXU/TdzcPObjuXd9I450bSfeeumFWAJ1P1NL90sR4xCYanS1i81ShHY7othaa7txIqjPLwpf1BvFMLwD6GTAyeaoTn4Bk/1Sz8RHxTAviKvef1Oc3srWZtt/sOa59eGbMIDkCT2xsTzuNd24YVaEdbX6klSd/deJTIZzY4XFS+qGF9j93ZcD+m7PUCxk9qhxRq486CtyGkHSpMeXE6mSG5ru2cuy29TR+Z2Ofx98asOcJJ7ZFrCurOvFoCjt1J3CEjBDoicyL7VlTb88Bm+4e3BPrnVtEqiM2qnP4SyKa6J8TO/KpTLTWfObXm3OmsZho6tr6PFob68Y2jZM18A/5XAS9xrn3jsFjmdbaDiNW5jQ7YxGxf9h4hLO+/gKxZJq2cIKyoIdpRX5aw3FbtFpYnlhrWpQ5FSFiyUxfiGgyjd/jylu5eCJ5Zmcjn/7VpjEb/Jt7Ytx412vUd+R+GyzRWuhzG9M/HaeI1Vqzqa6TM00vLJie2AlyVBwy+2ssmaG55/jfh03dMaYU+jhzZgkZDVsO509F+pffb8nJw7UiGM6aVWIbR4zK5I6c2iF2bZARWvNbzf5aFvLa+dz56IomSaQyTC3yURr0DLluNi09cW686zU7n3ekHGmPUFMaYE5FkNqWXrTW5hQ7xnPjdDgkJ/Zkku6MgwZ3ZQBHyD2m6pd/SWityfQkcJX5UR5HjpBxlftItUaHnG83WR8mE0naoX2nIjqj6V3fSPcLh+05Jk8kmczxHTPZErWnJHF4R++Jje5qIxNP51QozfHEzjBEbKafiB04T+zEv9gsEeeaEsBV6jMG2SepKnL4laNEt7YcV5/xzKwhcWR4Eduzth4yfVPPjISMFU7sc9oe+ux75CgoQLndpNtOzZxYV8nop9mJ13YB4KkKGYNjvxs9Bk9s75tNJA51454WJNUcJbbPCD+z54nNV9jJEnsu829q5CJWa026O973rE2gJ7Z3fSPhN46h08a7PZ8nVnmduKtDkJEq/oPRv7BTgc8wQIxELKzd38oT245x5zN77dDEiaauLUKhz0VZ0DOqcMpsrJxYK5wYGFNe7OG2CFOL+t5lDjU2MVTod9MajrP7WDdtYcOzNa3Y2G9TP0NQPNnPE1thGOZrW3pJZzSJVAa/25m3cvFE8sdN9Ty5vTFvCPRIWLuvlQ2HOnhpb0vOcit8uNDvNqZ/Os5w4iPtUVrD8VwRqybOK5edm5ltgBkrjd0xphb6WD6jGIBtDQNFbGckwUObG3hoc5+IPdIeobLAy+zyII3dMdIZbRo88qd7jNgTG45THvJSFhzaE2tNrzOl0EdZ0EPrCMcbrxxoYcOhDl7c0zyyBpkcaY8wvTTAnIoQXdEk7b0JkumM7XV2OZRMsXMysT7IzhIfrlLfpA5HPBHoeBqdzOAs9OAo8OSIWN/iMlzlfnrW1A8qYBINPbjNAeWpSqotaoRapjKEXx+fIgIjJZ2O8/q6Szhy5IExbZ+JJEm3x4wBJ4YnlrQe+eA5lSG2rwPfotIc4WoP0BX2vrPvvdZZQvcEFnZKNUVQHgfOIi/OMh+ktWGYOsGkexIkjvQAkDw69g+se0YNqcZGMvHBzyFxpIfEwW5C76q2q96OBJ1V2El5naBzw1uVUjjLy09uOPEQWIJ9NCkfsd1tOAo9dp91BFyjnmJHZzThtQ24q0OU/fUSHAFXX25uX1nVoT2xSqFcDnRqZM9EJpKClMZTY0Y9dE9Mn87EUqRao+hoisSRbtDk9cQ6Cz24KwyvuIQU5yeezPSbYsfyxA4vFn66pha/20lbbyJnwDyR1LVHmFkW5IyZJXZu42gJx1IoBQG3k+BxiNjtDV0sqy6yf3coxVjS7Jaa+9jR0EVrb8IMJzaKDh3tV9zJzom1PLHllogN26HDAY8zb+XiiUJrbd+LHQ1dY9rHjqPGdjv7bd/fE9tznJ7YTYcN7322iHU4FBk9MQbsurZegubzVTdKb2J/UukMLT1xphb5KPK7mVkWyHu9dx7tBox7YZ3TkY4IM0oDTCv2k85oWnrixPoZsKCvQNlIaTP7a1nIO6QwtabXmVLoozzkHXFO7I6GvnMZKemMpqEzyoySgP18HGztNT2xWSJWptg5eVieV1eZz6h+OcQASevR56GNJzqjT/q8rH3heR6cIU+ON045FKELqkk2hG0PSDY6lSHZFMFjDihPVZLmANVV4ad33dEBVVzHC50emHva2PQwsVgDweC8Me3TGlxb11iZL/2RhhTHD3WhY2n8i8ty/2CVUy/wGIXP1MBwYrt2QR4RO1HPTbIlgqsygFIKV5kxWEm1n/jiM7HdfeF4iYaeMe/HUzMDtCZZXz/oOj1r6lE+J8Gzp+QsH+4aZ+Jpe7oXhxn6N2Cu2PLyUYcTn6j3orPYC2rknkDDINOJf3GpbTRz+F2jDieO7Wkn1RKl4MJqlFK4q0P2OyI7J5Z+88Rmi1gAXI4+7+wwWOHDrvIAyuvMqRA8FKP9RmQbXKJmH843R6yzyIurwny+JrC4UyozxsJAqcyoimblHHMcipKk0hkS6Qw+V+4UOzC8J3bn0S5eOdDKZy+ex7LqIu5ZWzthxXGyqWvrZWZZgJUzSzjUFhm2mEw+umMpQl4XDoeyPbGjLe7UGUlQ3xG1BSiAwzFyIZQdJlxlhli+XttGIpWhPNjnie1fodiaJ9YSHxUFXkJeFwdbe4lalYvN76ff7bSXTSSH2iJ2Zdoxi1hzu+39ReyAnNjj88Ruqusg5HWxYEqBvcyaO3QivLF1bRFWzirF5VDH7YltDSfIaEMIAiytKhpwvaDvGrb1Juxqxkfao8wo8VNlRg4c7YrmFbGjye1OpjN0RpKUBb2Uhzy098YHfQf0eWLN/NkRVifePki/GIqm7hjJtGZGqT8nUiHVf55YCSceH6K72mi5Z/uAYkNDkW6PgVPhLPTiKvGR7ozn/RhmEmmavruZ8CuDDy4nmvj+Do7dtp5k08TPhZloCNN811aSzbkWL0u0OkNunIWeAaGTwTMqcQTdhF8ZaE1ONvZCWttekf6kOuM0372V2Fsnd5qUREMYXIri6+aR6U0R2dw0IceJvNlE4x0b7aItWmc4fPheCkKnUVJy3pj2mTxqDK6tQmW2WBmhiI3tageXA+/84pzllghwFnuNqschd04+dE5hp37zxPasrqfxzg0TYoBJNUVsD5GrzAw3PQl5sdHdbTiLDW9wcgwVdC08NVaF4vy5camOGNEdrYTOmYbD2xeS3/rATtr/sHfIfet4yvDA0mfcGFDcqayM1CiqE2eiKZq+vYnw60dHvM1YUU7D4z7c/e16+hAdDx8gdqATnUjjyzLIjEXE9qxtwFnsxb/UKHzlqS4g2RRBJzN94t2hUGaOkM5YhZ1MgWuKWOVSI46IsN4JziKP8Z7tjhPb30Hz3VvRQwymY3vaabx9/YhzpW2PsoLYrjbzmP08sV0JI/LG58IRchM/2D0hnpY19Wu46HcXUd8zum+sTmtaf76Tjj/tH7ZdWmtaH9xF7ybjnf7cribOvPV5GkcRunnP2lq++MetOcti5n31e/qGWkV+U8QOkxN77ysHCXqcfOScmXz8wjnUtvaOKuTvtQOtfPCu10dUeOhna2r5zK83k0xnaOiIMrMsYHvSLvvuGs75xvPsa+ozwmmt+exv3uTB1w8Bhpdy1R0vcc43nuecbzzPbzccpsB8D1m5o73mO+W1A62cf9uLnPON5/n4gxsHvTeWh2hpdaG9bKThxNFEmou+9TK/MNunlGJpdRFr9xuGuGxP7JF+xs2YXdjJYW87pyJIbWuvPWVKwBQmfo9z3OeJzWQ0f/fzDfxpU19/t8K6Q15XjtgIx1PceNdrrKsd+t2czmh2Hu3GoWBfUw/xVJpfrqvjc7950zamFPjcFPhcdEeTA+7JHc/s4ZtPDT9PeSSRYu3+Vt5RU2x75KBvvtiRiJrfrD/MJ38xstxfrTWH2yPMrQhRXeK3izyNFUuQTrVEbHURR9qjdEZyx7PbG7psm/z2+i6S6QzHuqLUlAaoKjb61bHOmDEdU38Ra7d9+PZ0mEK0LOShLOgho6Gz33vjmZ2NfOzeN9h9zHheKgvMnNgRhBNnMppdZr/Y3xweccqCFcJdUxpgekkAj9PB7kbj+C4zCszlcIyLIXA43nYiNt8LpWd1PfEDnbT+fCeZEVoDU+0xe2DuKvWBJm84osPjxOFzEtnSkmcvY0MnM6Ma2Ee2tKATadvjNFGkWqO03r+DxKHuAYPTtClcnAUeHCG3/buFcjsJnjWF2N72AQK3v5cQTM+B+UBFNjaSONhN24O7xjSVhr2/4yw8kmwI454axDu3iMDKKThLcvMODav/8PdtOA9uZEsLKKPoDEBr20tEIm9RU/P3g4Zba61zBrH9j5FoCOMs8eIMGvu0xUq/9XQyPeDjobUmuqcd37zigdN5WJ5Yc4DrDHmGKOxkmSCN/5ylXtIdceK142ucyMRSpLsTuKYYItZZ6AWnOiEiNtvzk0mkiR/oxLe4FE91yO67Y/Hgu00RmxwkLzaypRk0BM+dlrPcWeghtrMt7zEzmaTxL562jRrW//099KFVqyh496oRt9fhd6HcDiJb+96L2W3QWo9rJIN3ViGxfR2DPuOZSJKetfX0rjtGx+/3otwOfHP7DDKOgHtU1YlTHTESB7sInjPVDhd2Vwchow1jYkaDwzTcDOOJNcKJR+eJdRZ6cRZ5SXcnCL/SQOJgN9E9gxfhiWxpRiczI86VTh4NG6HCU4P2HLDZnlid0aR7+oo9hc6dRmxPO93PjW0u46E8pnOK5tCd6ObpQ0/bywbrO9nvQeVUeGcVEtncTNdTh4Y8fuJID7FdbfSsMYTD3IogXdEkj28bmREmlc5w1+pafr+xPqdASr4pNvo8sYP3t0gixVPbG7l2RTVFfjdXLp1KecjDn98ceUjxva8cZP2hdp7fPbTw/cW6Or7+5G6e2HaMp3c0kspoZpYGWTGjmE+vmstlS6YiYFiLAAAgAElEQVTw7oWVtkcV4PXaNh7bepS7Vxve4d9tPEJ9R5RVCyp598JK3reimi+8dyFAXzixKaa/89w+EukMC6YU8NyuJnYfyx+hYom1pVWjDyf+46YjNHbHWDStTwAvrSq0qyaXBj34PU6WTCscYBiwPLGWiAUjpLi2pc8T659AT+zmwx28sKeZO57ZS8J8L2yq66DQ5+LSJVPYYYayAjy7s5ENhzr4+auHhtznwdZeIok0715YSTKt2dvYw09efotHtx5lU10HDgVBj5NCv5uUmctpEY6nuGftQe5/5RBdQxhekukMn/7VZo60R7jl/Fk5f7NErGWAGEz4x5Jp7nxmL0/vbLRDdoeiJRwnkkgzsyxATWkgJz92LFhGKysP2zKg9G/LzoYuLphfgUMZHu6jnVEyGqaXBmzjyLGuqD0dUzajmSfWCh8uN8OJgQFhwj9+6QBr97dy7ysHKfS58HuclIe8hOOpYUXpobZewvEU715YSTqj2dM4smgx6x03oySA06FYOauEZ3caBkDrXk9kReps3lYiNpXOcN2PXuHbz/Z5HlLtMRJ13fgWlZI8Fqb9N3tHZJVNNUfsgiFO88Pf/KMtHP36ugF5SIHlFSSP9ZJs6iV+uJvmu7eSylO6fUTn0BWn8TsbR+zl08k00Z1t+JeW94WnYVqhf7GL8PqhczebfvgmR7++jmO3rR9yEJQOJ2i5bwdkNJ6ZhUS3t+YItpxw4gIPOpoaMG2Ef3klZBhQ4Cl5NIzyOe3rrNOalru30fnkQbTWRLa24K4K4vC5aL1/x5AVjKM7Wjn69XXGOX1rA2AMeJq//yY9L4+9qrDWmsTRMJ5qI2+39AML8C8szVmnZ3U9Td/fPKShpPvlIzR9b/OgU2qkexLE3+oksLzCFqyHD9+Dz1tFZeUVg7at9f6ddDx0AIBUZ4zG29bnXOdkQxhPVZ+RYDCx0vl4LS0/3ZZzb1NNEdLtMXyLc8/X2JHxnyViHQX9vPBDzBPrX1SK8o6vAQiwowSsCraWISrdFqV3cxOtP985IZVU0+EETd/bTO8645mL7W1HJzP4F5fhqQ6R7oiTDido/uEWup49NKp9O0tKcASDJOrye2IjW1rwzCy031kWgeWV6GTG9qZlU1//IG+sv5JkohPl66taDQONGyU3fYiKz31uVG0OrKgkeaSHVFuU6N52Gr+1gWSjUcWw7Ze76fj90B7i0eBfUYmOpojtz5/DF93ZBmmNb0kZmUgK7/wSe3obMER3ujdpGxDbfrnLfo9Y3rlsLHEeWF5pL/NUG+FziYaw8fyYHlhlFm/qWVNP4x0b7JQK5TIjFEYjYrsToMBZYES8JFuixPYbRqDBnqNMPE1sdzv+0ytGnCudaOjBXR3qi45xKhymAQwg05uEjLaLPRVcXENg5RR6XjxCeN3ove/hV4/S9H+b83rDpxdMZ0XFCp48+CQA7X/YR9sDO/N+x6NbW2j89iY7tLzg4hqC75xGeE09vRsaBz1+1Lx2qaYIycZe5lSEOH16EY9uHdm5rKttt8Nus7exBuverIFsaATViZ/b1UQ0mea6FVWA4d24ctk0nt/dRE8syZPbj/HBu1+nudvw9vzDLzZy1+q37O07ehOs3mec06NbBj+HJ7Yd478e2cGFCyrwOB388EXjGzKzLIDL6eCLly/itvefzm3vP932MIHhuQVo6Iyysa6Dx7ce44L55dz+gdPt9W84Y7pxvlk5sZvqOthY18GnV83l/256By6HGvQa7zjaxfQSPyXBPuPJYNWJtdb87+O7+OQvNhFJpLjnlYOsmFHMyqy8zOzc2nJTEFy7oootRzo5nOXBs8KQs8NA51SEaOiM0m56xiwRG/CMLie2uTvGh+5+fUjjiHU9Grtj9nqb6zo4Y2YJy6qLaOmJ02R6DB8x7+2Le5uHDE+3QolvOtswhj7wWh0NnYZx6vndzRT63Sil+gwsWc/hc7saiaeMsPhnduZ/hjIZzZf+uI2X97bw9euX8Z5FuSktVohpOJ5Ca83fPbCBL/xh64D9PLS5wQ6DHerZu//Vg/z1fes50GQYhmvKAswqCx6/J9YMLc8OJ4bcUNvuWJJDbRHOmlXCvMoQO452s99sx4ySAIV+o3J1Q2eUWDJj51ZbKEZe4NKqMGxNsQPk5MUeau1la30X71lUidOhqDTbXWY+M+3DhBRv79cvRhpSfKA5jFLY74Rrl1fZ/ckq7JRvip3uWJKP3LOO53cZ39SX9jTzgZ+MvjJyNm8rEfvr9YfZ1xTOeVlZg43ia+dSdMVsYnvaiQ0h1sAInUw2RvCfZoSbeWcWErpwOv6l5fgXl+WIRQD/6RWgIPJmC51/PkDiYPewlt/BcBYaIrDrmUMj8hpHd7ejE2kCKypzlveuP0ZsZxtdj9UOOQ2Db16JcU5OReef9w9q4Xb4XfjmFVN+y1IKLqgmE04SzwrvzYQTxkDH7zJyI2GAx9U9NYBrSmDAYCthCixLtCmnwj0tSO8bx4hubSHVEiV4zjTKbl5CJpKk+/nBrf3OIi/+xWX4F5fhW2h8wBweJ64KPz2r60mNcUqKdFsMHUsPGvKc7o7T8/IR3OX+nHDO/nimF5Buj9GTJ6waILKtBTT2/dQ6TWnpu5g9+3M4HO682yil8FQFibzZTOJID11PHyKTyOC25m2NpUi1xezfIb9YSTb20ru+Ec+0kO1dAsChCJxRiT+PiFVKUfL++YRMD6CzIDcfOneeWGuhGU7pduI/rYzojtZxFZUpU8S6sqZhcZX5SRwN0/nIW8T2tBN+bfzDXB1BN85ir129uvvZOlxlPrxzi+ww7o6HDpBqjtjTEY0UpRTumTUkjgwUscnGXlJNEQIrKgb8zTOrEGeRZ+Azl2jj4KEf4PdNJzR3Bv5l5cZxBgknHgv+5cY+ezc30/VELQ6vE1e53+yvIaI72sbNC++bX4wj4BpUyEW2NOMq81H20cUUv28uRZfPyt1+UalRsO21BiKbmojuaMM7qwj/4jJcJd4B+4tuacFTU5Dj2XSWeFF+lxE2ntE5z1DR1XMIrKgkk8jQ/YL5/srxxI7MYp3uiuMIuY0Q6kIvOpqCjMY7t4jYnva8IjC2qw2dzBBYPrB/5CMTT5NqieKpDtnRMc4CT05Btz6PsPGuV0pRcv18AiunjLpvp3sSdL94GFep354CrD9XzrmS/R372d+xH091iPhbXUaKQ3a7E2m6njpkP4dWu4qvmYt3ThFdTx3MW7xLpzWRbS14ZheCw4xqwBiYbavvorZl+OifR7Y0EPK6DOGbJRrzeWKdDkWB1zWk6Hhs61GmFvo4e1bfO/e6FVXEUxke2tzAVx7ewfqD7fz1fev51K828czOJu58Zq/d1qdMj+p5c8tYva95QDgkGGG9n//dFs6sKeHuj57JqoUV7DVDhmeWBQdt276mHl7a28I/XDQHn9vB/z6+i4bOKNetqM67fnZhp5+tqaXI7+aDK2dQGvRwwfxyHtt6NG+e346GrhwvLBgFgvIN/u94Zi/3vnKQp3c2cu0PX6WuLcInLpyTE7mUnVtrCYJrlhtGgke39n2PY8kMDtUnvKCvQvEu0yM3lnDirmiSm+/fwBsH2/nvR3bmvf/JdIYnth3jqmXTWDAlxE/X1NIVTbKvuYcza0pYNt0UVfVdtIXjvHKglfPmlpFIZXhmx+BGmh0NXXhdDlYtrKDQ5+KhN+vxuhycUVNMOqNt8WoXHctq2yNbjlJd7GdmWWBQg8g3n9rNQ2828K+XLuCvTEGUzTvnlqEU3LP2IM/vbua1t9pyxulgCOF71tayrLqIixdVDtovfr/hCF97bBdr9rVw6xNGiPPM0gAzywJ0RZN5+/pIaeyO43YqWwSWBD1ML/HniDvLILC0uoil1UVsq+/iBy8doLLAy4oZxSilmFbk41hnjNhxemKtkODSoIeyoOmJzZo6xxL6t75vKXd/9Ey+cJkR/dDntR36Wuxo6MJj9ouSgJsd9cOL2NZwnF+9cZj3LKzEY37Drlg6zRavOVPs9ItM/NGLB3j1QBtffngH7b0J/t+ft7OxroOb71s/4kJU/XnbiNiuSJLvPLeP8+aWcemSPitQdGuz4aEo9RE6rwpXhZ+uJw4OGrqkUxk6n6zFVeknePZUwBhkFF85m5Ib5lNyw3wcgVwx4Szw4J1XTM/aepLHevHUFBDd2kK8bvhwiP4opSi6eg6ZniQ9Lw+fAxTZ2oKjwIN3Tt8LIRNN0f1cHe7qEDqj6Xrm0KDbF10+yzivDywg3ZUgvCb/MZXTQckN8/HMKMC30PKg9YXhpLsTOENuIy/SErH9cpCVUgSWV5A41G17qnU6Q/JYb47AAii8pAblcdL+h33gUPiXluOpChE8exq9bxwbNP/XM6PAvk8l1/UVQSq6YjZaa7qfHvxaDEXiqBXynH+A1vX0IXRGU3Tl7CH345tXjG9JGT0vHcmbox3d2oJ7WrDPi6iczJ71GaqqbhxyvwWrZuAIuWn/7R6iW1oouLAal1m4wg7XzvLE9hcrWms6H69F+VwUXJz7EXJXBij94MIB1UktgmdNxVVuWOScBR7SPcm+cPhhqhMHVlSiY2lie8c2H2E+ks1RcKkcr6Sr1Ee6I45OpnFPD03INElKKYqumkMmmqLlrm2kWqIUXTUH5XTYYiC2qw3vvOL8Xu1h8MyoyTtXbGRLMziwhWhOmxwK//IKYvs6ciIYag9+n3Q6wrz5/0no/GoKV80ABvfQjwVXsQ/PrEJ6Xj5CqjlK0ZVzbANgwYXVOIu8dD5eOy450crpwL+snNiugaHT6e448dou/CsqjSJz51blzDML5nO5uJTuF4/Q9cwhPDMLKf3wIkpumI93Tm4eeLKxl2Rj7wBRqJSyw8Z1OpMjYgvOr6bkhvkUXz0bBuTEjs4Taz2HlhfUVRmg6PLZkNZEdw4svhXZ0oyzyItnZuGAv+UjeSxsTC9X1eeJzVfUyVje905QTiNCZbQitvu5OnQyQ9FVg787L5t5GU7l5KmDTxE8ZyquSj9dT9bmXLfw2gbSXXGKr5qTI7iVw/ymRlN0vzjw+YnXdpIJJwmdV413XomRnqM1V59ehVJDe4TAEKpP72jkvadN5QNnTmdvUw97Go38YMsb2n8gW+h30x01vFKPbj3Kpd9ZzU9efotUOkNnxPCiXruiCkfWeZxRU8L0Ej+3PrGL9kiC/7hiEW+1hHl5bwtfuGwBPreTbzxpzEf9yJYG5lQE+fcrFpFMa57KEjhaa/78Zj0ff3Ajs8uD3HvzWfg9TluEel0OKgvyv+vB8ML63A4+eeFcLlk8he0NXfjcjpxxVzYFpuf5m0/t4emdjXz03Bpb2F63opqGziibDudGUHRFk9S1RWzRZuFyKHYe7baFSjSR5muP7eTHL7/Fh8+p4b+vWcKB5jA1pQHee9rUnG2nl/jtfORSU6RUF/s5a1YJD285yg9f3M+533iBe16pxed25gjgOeXGc3CnGeUX8Ljs/zcf7mDlrc/xL7/bMqjn60BzmI/d+wYHmnv4jysW0R5J8CPT653NqwdaaetNcN2KKj5+wRz2NPZwwe0vorVR7XfJtEKUMrzUT24/Rjqj+crVS6gpDQzop/uaevjQ3a/z2d+8yRsH21k8rRC308HS6iK0hksWT7E9cJZ47fPEGt+KtnCctftbuWZ5Fdcur+K1t1rtKrhghL3/1yM7+Nnag9z8zpn843vyF548raqID5wxnftfPchXH93JvMoQHz7HOPbuY92svPU5zrz1OWpbe/nEhXO4dkUVx7pi3PvKQa770av855+30xlJcPfqt/j3h7Zxwfxy3r2wgl3HjHzO6SVGODHAt5/dxyXfWc3vNhy2DR77m3q46aev84+/3kxzT4zfbTjMJd9Zzc9fPZgjlJu6Y1QW+HKeu6VVRTkVnXfaudpFLK0qojUcZ+uRTr54+SLbQ19V7OeFPU3sa+oZMMWOVZ141R0vs/LW51h563N86pebbO96NlZ0R3mwzxNrCVOtNY9saeDs2aVUFfu5ZMkULl9q9Hnba5sleFvDcT73mzf50N2v2/ntOxq6WTy1wO4XVgVri42H2rn6B2tZeetzvPObL3DX6rfs6b7+48rF9npFATerFhqOF7uwU78pduraernv1YOcUVNMY3eM9//kNY52xfjS5Yto6Izy4Z+9wa6j3ayrbePK/zOOed43X+CetbUDrks2p8yknEqpy4H/A5zAPVrr20az/fdf3E9XNMmXr1piv3yMwUaE4mvnGsdwOii6ag5tP99J813b8lp9M5Ek6bYY5becNqppKgLLK4jv78Qzs5Dyv11K450baf/1HlxTArjL/XYbRoK3ppDAigp61tYTPHvqgBBBu63RFLE97YTOnZbz0e5+4TCZaIryv59PdGsLPavrCZ1XNeTgwju7CP+yciN/+HBfXLwz5Kb0gwtz1lVuB/6l5US3tdASNl52STPnErA9sZmegZbGwPIKup+tI7q1hYKLZpBsikBaD6hM7Ax5KHxPDV1PHsS3qNTO5Sy8pIbIlma6njxI+S1LBz2f/rhKfRS8q5qel81rMWPogVaysZfOJw/av1uFvtxTAgPWTdT3ENncTOii6SPKSy66cjZN391Ey8+24Sz24ZtfQsEF1aTaoiQO91B0xawRn5eFw+ei6L2z6PjTfhwFHgoumtF3LqaIzfYiW2Kl+/k6wusbIZUhXttF0TVz7Gs9FhwFbshoWu/bAQ5FoiFsXzPVL5wYwDu3GEfQTefjtYTfaLSvxfGQao7gLvfniAiruFPo3CqC506j6Xub6H6ujpLr55OJJGn7bV9oa/E1c+yiUKPFMy1I8Kyp9K5vzBGrjoAbZ6mPdEfMELZjmErKUzODnhdfRKfTKKeTjj/vJ9URJ3GkB++8EpwhT97tAssrCa9poPW+HYaX6p1xGo79munVHyEUnJ+zrmXc6H7hML0bmyi+es4AwTcaAisMo5V3ThG+JX3CXbmdFF0xi/bf7iWyuYngyqlD7GWEx1peSe8bjcR2tRFYUUlsbzs9rx41IgM0w3oijedyMzqepvjm0wa9R5GtRs66//SB+3NXhwivbTAiUxwDt/efXoHn1aMkDvdkVSdWg1YnjrzZTO+bfcbCRF23bbC0hGVgeQXu6SGcZT4iW1oIrpxqpxgAxA90GtMu5WlPPmyj1/SQ8Y105CnqZKbU9J87NpvoEBEPgWXlBM+aSuJYL70bGgmdVzXkM1fmL+Pcaefy5MEn+ew7PkvxVXNovX8nzXdttY3KiYNd+JeW5Rh0LTxVIYIrpxJ+7SjBc6bmHCuypQXldeJfVIJOpOn4wz4Sh3uYOrOQc2aX8sBrh9h8ePCIgXAsSU88xXUrqlhSVcjXHtvFZ3/9Jl63gx0N3Zw9u5Tz5uVWdS/wuVi9r4X3/fg1th7ppLLAy+1P7+FPm+vxu50k05prTS+hhVKKa5ZX8ZOX3+IDZ07nHy6ay5KqQlrDca5/x3ScDge3P72HD/9sHesPtfNPF89nWXURs8uDfO/5fbaQ7Ywk2FbfxRk1xfzko2dSZF6/ixdXEvQ4qS7x5wzi+3PmzBJmlQcpCXq4dnkVj287xsWLp9jCtD8+t5MvX7WYg629eF1OPn7BHPtvly6Zgs/t4Et/3Mb00r57Yk3Hs7Sft+7Tq+bx9Sd3c8l31rCkqpC3msM0dEa5+Z0z+a9rTsPpUJQEPNSUBXIKC1nXb1l1EVvrO+05YMHwuH/lkZ3c+ew+LlpQwfQSP0uqcg0+i6YW8LmL59MWjlPod7NomjF++PgFc5hW5COaSPPYtqOs3tfCaf3arLXmjYPt+N1OfvThM7jstKnsbw5z36sH2d0vB/FQay+FPhcXLaxAoTjU1ktnJEmR381Zs0txOx3MrQjxm/WH0RoWTAmxeFoh1y6v4scvH+Cv71vfd8zadgJeJ28e7iSRzvCxc2cCRlj1a2+1cc3yKs6bV8aXH96R5Yk1/v/aY7soCRrVcNMZzXUrqnA5FD948QC33L/B9vRZ1/+W82fljL3z8W/vXcgT24/R0Bnl/lvOwm2Orwv9btvgUBb0cMXSqSTSxly8X39yN+UhD9vrO/nDxiMk05rLlkzhux9aQVN3jLX71zC1yIfH5WCWOdXLL9bVUVng5Ut/2s6Dr9dRGvTwRm276TXv5JmdjSTTmsoCL199bBe/3XDEDsPd0dDFrLLc99Cy6UU8bRZPUkpxoKmHaUXGNDaWkWVZdRE3vKNv3PLpVfNsUf3+M6fn7O+y06ZwuD1C0nSkJVIZHt16lFcPtLKipiRn3bq2XlwORaHfZUz0oIxQ6hf2NJNKZ3irpZdbzh9o/Cs3vba3P7WH+8186e31nfTG0wS8Tq7+/iucM6eUzYc7+IDZvqXVRfx0Ta3dh5KpDOsOtlFV5Oe9p03lcHuE254yjGS3nD+LeZW5Y/Zrl1fx3K4mXNY8sU5jWiXruh1pj+B2OvjJR8/kfx7fxRPbjnHF0ql8atVcllYX8vnfbeHaH75CKqNtI9Shtl7b2z4Yp4SIVUo5gR8BlwL1wAal1KNa610j2V5rTTyV5q/Orsl5+aTDSVyVgRwPhW9hCQUXTSde22WEYg1sCwWrZuBbODpPiX9ZObE97RReMhOH10npjQvofuEwOpoak1ej8PLZJI6GSXfFBxWx6e447qnBnFBiY1qLDMGzpuKpCuEq9RHd025bzoei6KrZ6EQ6p8BJxpVfyIfOrzLmTTWvoavUR+AdRjuc1vyLroEvNFeZH9/CErsym06kzdyrgaIydF4VyWO9tkccTHF7cQ3x2i4yifTAQkNDULBqBrE97SOqUq3TOqd/OPwuQkuqB4SSA3YRocJ3zxjwt3xYRo3IxiYjd9jMwUn3GPvxjzDsrz+BM6eQaAjjX1Jmi1QwPD2e2YU5IsdZ7DXyA3sS9nkGzpxihwWPFd+8YqKzC20Pr7vcT8AKVXU7cc8oyIlkUE5F4aU1RDY1m3nUx+8BdITcAwrY+BaW4j/UTcHFNTiDbkLnVpFJGEWstCb3XXCcnsHCy2aikxkK3jMj56Ne8K5qdDKNZ9rgoXpD4V20CP/pp5Pp6cFZXEwmnkZHU7gr/BRcOH3Q7dxVQQJnTiHVHDHm/Uw2EwzOZfbsgTmuziIPvtPKyHQn7FDV4yGwvJL4/k4K3ztrwADHv7wC74ZGMrHxKYzimVWIe0aBnW9uPcPK5TCEyzBi3F0RoPi6uWSiqWGNXP6l5baxLpvA6RUk6roNw9zsgZ5PpRQlN8w3wmfN97p7atAu+tQfncrk9E13ZcB+33tmFeFfWkbwrKkopQieYTz/2pzWytrOM7OA0DkjNxIop8IzsxBHgcf4Hl40Y4ABVLmduKtDOAYxnADQr+39zwsg0x3HVRmg8OKBIYj9uWrOVTx84GG64l0ULyylYNUM4m912sfwzimi6Ko5g25feNlM4nVdpLsTOSJWuRSBd1Ta6Q09qwN22PGnV83je8/vG7aS8JXLpnLe3DJcTgefvGgOrx5ow6Hgf647jY+eM3OAKLzhjGqe3N6IUvCfVy7ib8+fzdM7G3nwtToS6Qw3njmd06oG9p+PnFNDbUuYL5pFky6Y3/etuOX8Wexr6uFgay/nzi7jQ2cZ759/vmQ+9796yD4Hl0PxlauX8DfnzcoRej63ky+8d+EA8defm7LCRS9aWMF1K6r4u3cNHYH09xfkvy9Br4vPXTyfZ3c2DbjGFy2oyMlpBfjbd83m7Nml3PnsXjojSWaWBbjjxtM5b27fGO997xjcCPrRc2dyxrHcfV67opp1B9u55vRpXL40//fP4VD8y6ULBix/59wy3jnXMFB84qI5fOvpvXm9sVefPo1/v2IRlQXGM//FyxfSGUkMmPezNOjh5vNm2SL73967aMC+bj5vll25+O8vMK77TWfPYGNde841vG5FFV+6YhEdvQm+/+IBW0xdu6KKpu4Y715Ugdfl5F8uXWCHz86vDHHRggq6okm6o0lcDgc3nTWDRVMLUErx4XNq2HW02z7OrPIA3/ngcs6Z02/qvTxUFvq47f2ns+dYN+9e2DdmrS728/Xrl+Ws63I6+NfLFtDYFeOfLpnPgeYwP3zxANe9o5prTp9mVowO8eWrFpMwxeDs8iA3njmds2aX8oEzpvOrN+p4eMtRemIp+1p0RhLc8cxe3jWvnI+cM5M/bq7n9xuO2OdTUxoYIDrfe9oUVu9rsYuCVRb6uHKZ8T5dVl3EVadP41MXzc15xrP7RX+mlwT46rWn5Sz71Kq53P70HnuaHIuSgIdVC4z6KE5lPP87Gvqu/wXzy7nm9FxjF0BVsY/LT5tKY3fMXvfMmaV86fKFlAQ9fOvpPexrCrO0ush+Xq5aNo2Nh3L70N+eP5vPX7qAkNeF1prHzeJv/3Tx/AHHvHTJFK5/R7WdAvGueeWs3d9qX7civ5t/vmQ+Uwp9/L8rF+Mx77FxHhU89/mL+NYzeyjwGesFPC7T23yU62/PeykBUBNREn+0KKXeCXxVa/1e8/f/ANBaf3OwbVauXKk3btyYs0xrPSYPx6nK8ZxP9rZyXcZn25OxX2Fi+Eu/X6fK+Z8q7RBOHieyD0h/EwRBmFwopTZprVfm+9upkhNbDWQnq9Sby0bF2+3jdDznk72tXJfx2fZk7FeYGP7S79epcv6nSjuEk8eJ7APS3wRBEN4+nCoiNt+XZYCLWCn1CaXURqXUxpaW8Z2WQxAEQRAEQRAEQTj1OVVEbD2QnVA4HRhQEUJr/VOt9Uqt9cqKirHlDQqCIAiCIAiCIAiTl1NFxG4A5iulZiulPMBNwKMnuU2CIAiCIAiCIAjCKcYpUZ1Ya51SSv0j8AzGFDv3aa13nuRmCYIgCIIgCIIgCKcYp4SIBdBaPwk8ebLbIQiCIAiCIAiCIJy6nCrhxIIgCIIgCIIgCIIwLCJiBUEQBEEQBEEQhChUT9wAAA3XSURBVEmDiFhBEARBEARBEARh0iAiVhAEQRAEQRAEQZg0iIgVBEEQBEEQBEEQJg0iYgVBEARBEARBEIRJg4hYQRAEQRAEQRAEYdIgIlYQBEEQBEEQBEGYNIiIFQRBEARBEARBECYNImIFQRAEQRAEQRCESYOIWEEQBEEQBEEQBGHSICJWEARBEARBEARBmDQorfXJbsOYUEr1AHtPdjsEYRwpB1pPdiMEYRyRPi283ZA+LbzdkD4tnMrM1FpX5PuD60S3ZBzZq7VeebIbIQjjhVJqo/Rp4e2E9Gnh7Yb0aeHthvRpYbIi4cSCIAiCIAiCIAjCpEFErCAIgiAIgiAIgjBpmMwi9qcnuwGCMM5InxbebkifFt5uSJ8W3m5InxYmJZO2sJMgCIIgCIIgCILwl8dk9sQKgiAIgiAIgiAIf2GMSsQqpe5TSjUrpXb0W16qlHpOKbXf/L/EXP4vSql7s9b7iFLqiUH2fblSar1Sao9SaotS6ndKqZqxnNTxopT6pFLqr0/GsYWRoZSaopT6tVKqVim1SSn1ulLqevNvq5RSjw+z/VeVUl8Y5THDgyxPm33W+jdrNPsdZt/j1heVUvOVUo8rpd4yr9lLSqkLx2PfwvGT1Y92KKX+oJQKnOw2WSilyrL6d6NSqiHrd88o93WrUuqfx6ldv1RKvW+M216vlPq38WiHIAiCIAgnltFOsfNz4IfAg/2W/zvwgtb6NqXUv5u/fwn4PrBRKXU+sBO4Fbi4/06VUkuBHwDXaq13m8uuBWYBh0fZxuNGa33XiT6mMHKUUgp4GHhAa/1hc9lM4NqT1KSo1nrFROx4vPqiUsoHPAF8QWv9qLlsKbASWNNvXZfWOjUexxVGhd2PlFK/Aj4JfMf6o9nvldY6M54HHcn91lq3AVbbvgqEtdZ3jmc7TjRa6z+f7DYIgiAIgjA2RuWJ1VqvAdrz/Ok64AHz5weA95nrp4BPAz8CvgXcp7WuzbP9l4BvWALW3PZR83gopeYqpZ42vUdrlVKLzOU/V0p9Xyn1mumR+4C5XCml7jA9GtuVUh8yl69SSq1WSv1eKbVPKXWb6R1eb64311zP9tIppeYppZ5XSm1VSm022zJNKbUmy2tywWiuo3DcvAdIZAs8rXWd1voH/Vc0owQeVkptU0qtU0qdnvXn5UqpF80Igo+b64eUUi+Y93q7Uuq6sTRQKTXL7KubzX/nmcsH7TtKqa+b/WydUmqKuSy7L64w/7ZNKfVn1Rfx8LJS6nazH+8bpD9+BHjdErDmNduhtf551nF+qpR6FnhQKeVTSt1vXoM3lVLvNtf7G6XUD7Pa/LhSapX5c1gp9W3zfF9QSuWdnFoYEWuBeWY/2q2U+jGwGZihlPor877sUErdbm2gjGiWzWYfesFclrf/57nfTvOducFc9x9G01il1M1m/9uilPqxUsphLr8qq03PZm2yzHwX1yqlPmOuO888p3uVUjuVUk8pw/iCUuoMpdQbZtv+pJQqytOGS83jb1dK/UyZHmKl1LVKqb3m8/gDpdTD5vK/V0p9z/x5ilLqIaXURvM8zjWXv8ds+xbzPIKjuS7C5MJ8Lp4232uPmz8Ptizv8pN9DoIwEqT/Cm8HxisndorW+hiA+X+l9Qet9WvAbuASDCGbj9MwBmiD8VPgs1rrM4EvAD/O+ts04F3A1cBt5rIbMLwGy83j3qGUmmb+bTnwT8Ay4GPAAq312cA9wGfzHPtXwI+01suB84BjwIeBZ0yvyXJgyxBtF8af4fpLNl8D3tRanw78J7lRBKcDVwHvBP5LKVUFxIDrtdZnAO8Gvq2UUsMcw6/6Qist704zcKm5nw9hRCXA4H0nCKwz+9ka4ON5jvMg8CXzXLYD/531N5fZj/+533KLkVyzM4HrTO/2ZwC01suAvwIesATFEASBzeY5rx6kHcIwKKVcwBUY9xhgIfCg1vodQBK4HcOQswI4Syn1PtNg8DPg/WYfutHcdqj+n32//w7o0lqfBZwFfFwpNXuE7V0KXA+cZ/ZrF3CTUmoq8BOM52k5cFPWZguAS4Fzgf9RSjmzzvV7WuvTgCimQRT4JfCv5nnsBb7Srw0B4D7z/JcBAeAT5vIfA5cBFwJTBzmN7wPf0lqvBD6I8T0A+DfgE+Z5XYjxfhDe3tyktb5aa301fX0237KhlgvCZED6rzCpGW048ahRSoUwQhbdQAVQP8z6ZcALGIOQnwJ3YYjHP2RpCW/WJg+b4XW7lOm9whC1v9Fap4EmpdRqjIFZN7DBEtxKqbcAyzuwHUO0ZLelAKi2ws601jFz+QbgPqWU2zy+iNiTiFLqRxj3PGEOwrN5F/B+AK31i8rI7bO8OI9oraNAVCn1EnA2RsjtN5SRK5oBqoEpQOMQTcgXTuwGfqiUWgGkMQbtAIP1nQRg5fFuwhjgZ59jEVCstV5tLnoA+EPWKg9lbTtriLZa+/szMB/Yp7W+wVz8qHk9wLhuPwDQWu9RStVlncNgZIDfmT//MqtNwsjwK6Ws/rAWuBeoAuq01uvM5WcBL2utW8AOO74Qo4+t0VofBNBaWxEzQ/X/7Pt9GXC6MqNZgCKM/nFwBO2+xGzXRvMd7QeOYIjQl7TWdf3aBPC41joBNCul2jG+DQAHtNaWeN8EzDK/CT6t9Svm8geAX/Rrw2Jgv9b6LfP3BzGE+Tpgr9UGpdRvgHw55pcAC7O+MSVKKT/wKvA9pdSvgT9prfPmrguCIAiCcGIZLxHbpJSaprU+Zno8m7P+9jWMAW0T8F36PATZ7ATOALZauVfKCKEMYXiLO4fIOYxn/az6/T/c+pms3zMMvB5596O1XmOKnKuAXyil7tBa988TFiaOnZgDcwCt9WeUUuXAxjzr5ruHut//2cs/gjGgPlNrnVRKHQKG80Dm4/MYfX45Rh+OmW0drO8kdd98V2lG/2xa/XiwbXdiiB3MdlyvlFoJZOc19mb9PNgzlCI3gmOoayPzd42OAcYQU1SN5L4o8l/vofp///1+Vmv9zMiaOuAY92mt+3tHbxikTZD7Hs7us/mWDxcJYbVhNMvzrXe2KayzuVUp9SjG87pBKbVKa71/hPsUBEEQBGGCGK9w4keBm82fbwYeAVBKLcP4+N+O4VWdqZS6NM/23wL+n1JqcdayAIDWuhs4qJS60dynUkotH6Y9a4APKSPPqwJj8L5+tCdlHrtemdUvlVJepVRAGUWEmrXWP8Pwlpwx2n0Lx8WLgE8p9amsZYNVcl2DIUxRRu5mq3lfAa5TRu5nGbAKw0tahHFvk8rIA505xjYWAcfMKIGPAU6zDWPqO1rrLqBD9eW7fgwjZHek/Bo4XxkF0yyGqn6bfd0WADUYYZyHMIxMDqXUDAzvtYUDsDx5HwZeQRhv3gAuUkqVmyG4f4XRD143l88GIxfWXH+o/p/NM8CnzAgBlFILRpH/+TzwQdOQZFUyrsHwYr7H7PPZbRoVWutWjGiJ88xF+fr+LmC+UmqO+ftHzXV2YnhYZ5hpAR8a4hw+Y/1iRlCglJqrtd6mtf4m8CZGuLMgCIIgCCeZUXl7zFCsVUC5Uqoe+G+t9b0Yuai/V0r9HUY14RvNAcNPgM9nheF+GqOIyIpsi7fWertS6p/MvxUAbeZ+rJy6jwA/UUp9GSNM87fA1iGa+meMPMetGJ6AL2qtG5VZEGqUfAy4Wyn1Pxj5aDcCFwD/ppRKAmHyh6cJE4TWWpuGhe8qpb4ItGB4lb6UZ/WvAvcrpbYBEfqMLWAYNp7AEGj/q7U+aoZnPqaU2oiRr7pnjM38MfAn0/jyEn1er1WMve/cDNxl5vnVAreMdEOtdVQpdTXwHWUUs2kCejAqhg/W/ruUUtsxvK9/o7WOK6VexQgx3Q7sIDfPthc4TSm1CehicMEgjBEz2uU/MPqUAp7UWltGw08ADymjqFIzRkj6Vxm8/2dzD0YY+mbz3d1CXz7qcG3arpT6GvC8eewk8Emt9QbT0PSIuc+jGLm+Y+FjGN8AP3CAfn1fax0xvz8PmeL+DeBnWuuEUuofMURqC4ahKp+Y/oy5/1swvosvmcu+YBqOMsA2+tJPBEEQBEE4iai+CEZBEISxo5QKa61DJ7sdgpCNUiqktQ6bQvpuYHu+SuaCoIzqrN/TWneavxdjFMvLt4x8y7XWXz3R7RaE0TJYX5f+K0wmxiucWBAEQRBORT5lFszahVF06mcnuT2CIAiCIBwnE16dWBCEvwzECyucimit7wDuONntECYFzRhpTRnzdwfw9CDLGGK5IJzqDNbXBWHSIOHEgiAIgiAIgiAIwqRBwokFQRAEQRAEQRCESYOIWEEQBEEQBEEQBGHSICJWEARBEARBEARBmDSIiBUEQRAEQRAEQRAmDVKdWBAEQRBOAuZcjecCKXORC1g3yDJGs1zmexQEQRDezoiIFQRBEISTx01a604ApVQx8M+DLBts3aGWC4IgCMLbEgknFgRBEARBEARBECYNImIFQRAEQRAEQRCESYOIWEEQBEEQBEEQBGHSICJWEARBEARBEARBmDSIiBUEQRAEQRAEQRAmDSJiBUEQBEEQBEEQhEmDTLEjCIIgCCeHZuBBpVTG/N0BPD3IMsawXBAEQRDeliit9clugyAIgiAIgiAIgiCMCAknFgRBEARBEARBECYNImIFQRAEQRAEQRCESYOIWEEQBEEQBEEQBGHSICJWEARBEARBEARBmDSIiBUEQRAEQRAEQRAmDf8fJzQ2KBSPvLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib --list\n",
    "pivot_table_city.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习：按照年份，讲不同行业的估值趋势进行描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3D印刷</th>\n",
       "      <th>2011</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">云计算</th>\n",
       "      <th>2000</th>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">人工智能</th>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <th>2000</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">虚拟与增强现实</th>\n",
       "      <th>2010</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">软件与服务</th>\n",
       "      <th>2001</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">金融科技</th>\n",
       "      <th>2000</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>11570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              估值（亿人民币）\n",
       "行业      成立年份          \n",
       "3D印刷    2011        70\n",
       "        2013       150\n",
       "        2015       150\n",
       "云计算     2000       290\n",
       "        2001        70\n",
       "        2002      3500\n",
       "        2003       150\n",
       "        2005        70\n",
       "        2008       290\n",
       "        2009       360\n",
       "        2010       360\n",
       "        2011       670\n",
       "        2012       880\n",
       "        2013       650\n",
       "        2014       540\n",
       "        2015       370\n",
       "人工智能    2003       200\n",
       "        2004       150\n",
       "        2005       570\n",
       "        2006       150\n",
       "        2007        70\n",
       "        2009       320\n",
       "        2010        70\n",
       "        2011       440\n",
       "        2012       360\n",
       "        2013       440\n",
       "        2014      1070\n",
       "        2015       610\n",
       "        2016      1310\n",
       "健康科技    2000        70\n",
       "...                ...\n",
       "虚拟与增强现实 2010       300\n",
       "        2011       400\n",
       "        2012        70\n",
       "软件与服务   2001       100\n",
       "        2002       150\n",
       "        2005       100\n",
       "        2007       210\n",
       "        2008       100\n",
       "        2009       140\n",
       "        2010       150\n",
       "        2011        70\n",
       "        2012       320\n",
       "        2013       340\n",
       "        2014       360\n",
       "金融科技    2000       220\n",
       "        2002       200\n",
       "        2005       300\n",
       "        2006       640\n",
       "        2007       300\n",
       "        2008       220\n",
       "        2009       210\n",
       "        2010      2520\n",
       "        2011      4040\n",
       "        2012       990\n",
       "        2013      2910\n",
       "        2014     11570\n",
       "        2015      1290\n",
       "        2016       210\n",
       "        2017       200\n",
       "        2018       200\n",
       "\n",
       "[212 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初阶方法\n",
    "data_exc = df.groupby(by=[\"行业\",\"成立年份\"]).agg({\"估值（亿人民币）\":\"sum\"})\n",
    "data_exc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>云计算</th>\n",
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       "      <td>670.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>人工智能</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>420.0</td>\n",
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       "      <td>1270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "\n",
       "成立年份       2009    2010    2011    2012    2013     2014    2015    2016  \\\n",
       "行业                                                                         \n",
       "3D印刷        NaN     NaN    70.0     NaN   150.0      NaN   150.0     NaN   \n",
       "云计算       360.0   360.0   670.0   880.0   650.0    540.0   370.0     NaN   \n",
       "人工智能      320.0    70.0   440.0   360.0   440.0   1070.0   610.0  1310.0   \n",
       "健康科技      250.0   850.0   410.0   340.0   340.0      NaN   140.0   600.0   \n",
       "共享经济       70.0  3200.0   170.0  4600.0   420.0      NaN  1270.0   270.0   \n",
       "区块链         NaN     NaN    70.0   900.0  1300.0     70.0   150.0     NaN   \n",
       "即时通讯       70.0   150.0    70.0   220.0     NaN     70.0     NaN     NaN   \n",
       "大数据       100.0   140.0   210.0   370.0   200.0     70.0   170.0     NaN   \n",
       "媒体和娱乐     150.0     NaN  1350.0  5340.0   400.0    520.0   220.0     NaN   \n",
       "房地产科技       NaN   100.0   440.0   240.0    70.0      NaN   370.0     NaN   \n",
       "教育科技        NaN    70.0    70.0   270.0   270.0    340.0     NaN     NaN   \n",
       "新能源        70.0     NaN   220.0     NaN     NaN      NaN     NaN   150.0   \n",
       "新能源汽车     350.0     NaN     NaN     NaN     NaN   1070.0   520.0     NaN   \n",
       "新零售         NaN     NaN   220.0     NaN   220.0      NaN   140.0     NaN   \n",
       "机器人         NaN     NaN     NaN   300.0   100.0      NaN     NaN   200.0   \n",
       "消费品         NaN   150.0     NaN   370.0     NaN    140.0  3570.0    70.0   \n",
       "游戏          NaN     NaN     NaN   440.0     NaN      NaN   100.0     NaN   \n",
       "物流          NaN   370.0   740.0   770.0  2610.0   1180.0   360.0    70.0   \n",
       "生命科学      270.0   370.0    70.0   220.0    70.0    800.0   300.0   550.0   \n",
       "电子商务     1250.0  1750.0  1230.0  1900.0   170.0   1700.0   580.0   140.0   \n",
       "网络安全       70.0    70.0     NaN     NaN    70.0      NaN   200.0     NaN   \n",
       "航天          NaN     NaN     NaN   200.0     NaN      NaN     NaN     NaN   \n",
       "虚拟与增强现实     NaN   300.0   400.0    70.0     NaN      NaN     NaN     NaN   \n",
       "软件与服务     140.0   150.0    70.0   320.0   340.0    360.0     NaN     NaN   \n",
       "金融科技      210.0  2520.0  4040.0   990.0  2910.0  11570.0  1290.0   210.0   \n",
       "\n",
       "成立年份      2017   2018   2019  \n",
       "行业                            \n",
       "3D印刷       NaN    NaN    NaN  \n",
       "云计算        NaN    NaN    NaN  \n",
       "人工智能       NaN    NaN    NaN  \n",
       "健康科技     150.0   70.0  100.0  \n",
       "共享经济     370.0    NaN    NaN  \n",
       "区块链      300.0    NaN    NaN  \n",
       "即时通讯       NaN    NaN    NaN  \n",
       "大数据        NaN    NaN    NaN  \n",
       "媒体和娱乐      NaN    NaN    NaN  \n",
       "房地产科技      NaN  600.0    NaN  \n",
       "教育科技       NaN    NaN    NaN  \n",
       "新能源        NaN    NaN    NaN  \n",
       "新能源汽车    590.0    NaN    NaN  \n",
       "新零售       70.0   70.0    NaN  \n",
       "机器人        NaN    NaN    NaN  \n",
       "消费品      150.0  150.0    NaN  \n",
       "游戏         NaN    NaN    NaN  \n",
       "物流         NaN    NaN    NaN  \n",
       "生命科学       NaN    NaN    NaN  \n",
       "电子商务      70.0    NaN    NaN  \n",
       "网络安全       NaN    NaN   70.0  \n",
       "航天         NaN    NaN    NaN  \n",
       "虚拟与增强现实    NaN    NaN    NaN  \n",
       "软件与服务      NaN    NaN    NaN  \n",
       "金融科技     200.0  200.0    NaN  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进阶：pivot\n",
    "data_pivot = data_exc.reset_index().pivot(index=\"行业\",columns=\"成立年份\",values=\"估值（亿人民币）\")\n",
    "data_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['金融科技', '共享经济', '电子商务', '媒体和娱乐', '云计算', '物流', '人工智能', '消费品', '生命科学',\n",
       "       '健康科技', '区块链', '航天', '大数据', '新能源汽车', '软件与服务', '房地产科技', '教育科技', '游戏',\n",
       "       '机器人', '网络安全', '新零售', '新能源', '虚拟与增强现实', '即时通讯', '3D印刷'],\n",
       "      dtype='object', name='行业')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pivot_排序 = data_pivot.sum(axis=1).sort_values(ascending=False).index\n",
    "data_pivot_排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>290.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>即时通讯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份       2009    2010    2011    2012    2013     2014    2015    2016  \\\n",
       "行业                                                                         \n",
       "金融科技      210.0  2520.0  4040.0   990.0  2910.0  11570.0  1290.0   210.0   \n",
       "共享经济       70.0  3200.0   170.0  4600.0   420.0      NaN  1270.0   270.0   \n",
       "电子商务     1250.0  1750.0  1230.0  1900.0   170.0   1700.0   580.0   140.0   \n",
       "媒体和娱乐     150.0     NaN  1350.0  5340.0   400.0    520.0   220.0     NaN   \n",
       "云计算       360.0   360.0   670.0   880.0   650.0    540.0   370.0     NaN   \n",
       "物流          NaN   370.0   740.0   770.0  2610.0   1180.0   360.0    70.0   \n",
       "人工智能      320.0    70.0   440.0   360.0   440.0   1070.0   610.0  1310.0   \n",
       "消费品         NaN   150.0     NaN   370.0     NaN    140.0  3570.0    70.0   \n",
       "生命科学      270.0   370.0    70.0   220.0    70.0    800.0   300.0   550.0   \n",
       "健康科技      250.0   850.0   410.0   340.0   340.0      NaN   140.0   600.0   \n",
       "区块链         NaN     NaN    70.0   900.0  1300.0     70.0   150.0     NaN   \n",
       "航天          NaN     NaN     NaN   200.0     NaN      NaN     NaN     NaN   \n",
       "大数据       100.0   140.0   210.0   370.0   200.0     70.0   170.0     NaN   \n",
       "新能源汽车     350.0     NaN     NaN     NaN     NaN   1070.0   520.0     NaN   \n",
       "软件与服务     140.0   150.0    70.0   320.0   340.0    360.0     NaN     NaN   \n",
       "房地产科技       NaN   100.0   440.0   240.0    70.0      NaN   370.0     NaN   \n",
       "教育科技        NaN    70.0    70.0   270.0   270.0    340.0     NaN     NaN   \n",
       "游戏          NaN     NaN     NaN   440.0     NaN      NaN   100.0     NaN   \n",
       "机器人         NaN     NaN     NaN   300.0   100.0      NaN     NaN   200.0   \n",
       "网络安全       70.0    70.0     NaN     NaN    70.0      NaN   200.0     NaN   \n",
       "新零售         NaN     NaN   220.0     NaN   220.0      NaN   140.0     NaN   \n",
       "新能源        70.0     NaN   220.0     NaN     NaN      NaN     NaN   150.0   \n",
       "虚拟与增强现实     NaN   300.0   400.0    70.0     NaN      NaN     NaN     NaN   \n",
       "即时通讯       70.0   150.0    70.0   220.0     NaN     70.0     NaN     NaN   \n",
       "3D印刷        NaN     NaN    70.0     NaN   150.0      NaN   150.0     NaN   \n",
       "\n",
       "成立年份      2017   2018   2019  \n",
       "行业                            \n",
       "金融科技     200.0  200.0    NaN  \n",
       "共享经济     370.0    NaN    NaN  \n",
       "电子商务      70.0    NaN    NaN  \n",
       "媒体和娱乐      NaN    NaN    NaN  \n",
       "云计算        NaN    NaN    NaN  \n",
       "物流         NaN    NaN    NaN  \n",
       "人工智能       NaN    NaN    NaN  \n",
       "消费品      150.0  150.0    NaN  \n",
       "生命科学       NaN    NaN    NaN  \n",
       "健康科技     150.0   70.0  100.0  \n",
       "区块链      300.0    NaN    NaN  \n",
       "航天         NaN    NaN    NaN  \n",
       "大数据        NaN    NaN    NaN  \n",
       "新能源汽车    590.0    NaN    NaN  \n",
       "软件与服务      NaN    NaN    NaN  \n",
       "房地产科技      NaN  600.0    NaN  \n",
       "教育科技       NaN    NaN    NaN  \n",
       "游戏         NaN    NaN    NaN  \n",
       "机器人        NaN    NaN    NaN  \n",
       "网络安全       NaN    NaN   70.0  \n",
       "新零售       70.0   70.0    NaN  \n",
       "新能源        NaN    NaN    NaN  \n",
       "虚拟与增强现实    NaN    NaN    NaN  \n",
       "即时通讯       NaN    NaN    NaN  \n",
       "3D印刷       NaN    NaN    NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_pivot = data_pivot.loc[data_pivot_排序]\n",
    "new_data_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
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       "      <th>2014</th>\n",
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       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>290.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>即时通讯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "\n",
       "成立年份       2009    2010    2011    2012    2013     2014    2015    2016  \\\n",
       "行业                                                                         \n",
       "3D印刷        NaN     NaN    70.0     NaN   150.0      NaN   150.0     NaN   \n",
       "云计算       360.0   360.0   670.0   880.0   650.0    540.0   370.0     NaN   \n",
       "人工智能      320.0    70.0   440.0   360.0   440.0   1070.0   610.0  1310.0   \n",
       "健康科技      250.0   850.0   410.0   340.0   340.0      NaN   140.0   600.0   \n",
       "共享经济       70.0  3200.0   170.0  4600.0   420.0      NaN  1270.0   270.0   \n",
       "区块链         NaN     NaN    70.0   900.0  1300.0     70.0   150.0     NaN   \n",
       "即时通讯       70.0   150.0    70.0   220.0     NaN     70.0     NaN     NaN   \n",
       "大数据       100.0   140.0   210.0   370.0   200.0     70.0   170.0     NaN   \n",
       "媒体和娱乐     150.0     NaN  1350.0  5340.0   400.0    520.0   220.0     NaN   \n",
       "房地产科技       NaN   100.0   440.0   240.0    70.0      NaN   370.0     NaN   \n",
       "教育科技        NaN    70.0    70.0   270.0   270.0    340.0     NaN     NaN   \n",
       "新能源        70.0     NaN   220.0     NaN     NaN      NaN     NaN   150.0   \n",
       "新能源汽车     350.0     NaN     NaN     NaN     NaN   1070.0   520.0     NaN   \n",
       "新零售         NaN     NaN   220.0     NaN   220.0      NaN   140.0     NaN   \n",
       "机器人         NaN     NaN     NaN   300.0   100.0      NaN     NaN   200.0   \n",
       "消费品         NaN   150.0     NaN   370.0     NaN    140.0  3570.0    70.0   \n",
       "游戏          NaN     NaN     NaN   440.0     NaN      NaN   100.0     NaN   \n",
       "物流          NaN   370.0   740.0   770.0  2610.0   1180.0   360.0    70.0   \n",
       "生命科学      270.0   370.0    70.0   220.0    70.0    800.0   300.0   550.0   \n",
       "电子商务     1250.0  1750.0  1230.0  1900.0   170.0   1700.0   580.0   140.0   \n",
       "网络安全       70.0    70.0     NaN     NaN    70.0      NaN   200.0     NaN   \n",
       "航天          NaN     NaN     NaN   200.0     NaN      NaN     NaN     NaN   \n",
       "虚拟与增强现实     NaN   300.0   400.0    70.0     NaN      NaN     NaN     NaN   \n",
       "软件与服务     140.0   150.0    70.0   320.0   340.0    360.0     NaN     NaN   \n",
       "金融科技      210.0  2520.0  4040.0   990.0  2910.0  11570.0  1290.0   210.0   \n",
       "\n",
       "成立年份      2017   2018   2019  \n",
       "行业                            \n",
       "3D印刷       NaN    NaN    NaN  \n",
       "云计算        NaN    NaN    NaN  \n",
       "人工智能       NaN    NaN    NaN  \n",
       "健康科技     150.0   70.0  100.0  \n",
       "共享经济     370.0    NaN    NaN  \n",
       "区块链      300.0    NaN    NaN  \n",
       "即时通讯       NaN    NaN    NaN  \n",
       "大数据        NaN    NaN    NaN  \n",
       "媒体和娱乐      NaN    NaN    NaN  \n",
       "房地产科技      NaN  600.0    NaN  \n",
       "教育科技       NaN    NaN    NaN  \n",
       "新能源        NaN    NaN    NaN  \n",
       "新能源汽车    590.0    NaN    NaN  \n",
       "新零售       70.0   70.0    NaN  \n",
       "机器人        NaN    NaN    NaN  \n",
       "消费品      150.0  150.0    NaN  \n",
       "游戏         NaN    NaN    NaN  \n",
       "物流         NaN    NaN    NaN  \n",
       "生命科学       NaN    NaN    NaN  \n",
       "电子商务      70.0    NaN    NaN  \n",
       "网络安全       NaN    NaN   70.0  \n",
       "航天         NaN    NaN    NaN  \n",
       "虚拟与增强现实    NaN    NaN    NaN  \n",
       "软件与服务      NaN    NaN    NaN  \n",
       "金融科技     200.0  200.0    NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot_table\n",
    "data_pivot_table = df.reset_index().pivot_table(index=\"行业\",columns=\"成立年份\",values=\"估值（亿人民币）\",aggfunc=\"sum\")\n",
    "data_pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>...</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>总值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>共享经济</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>290.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>670.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>云计算</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>...</td>\n",
       "      <td>440.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>消费品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>生命科学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>410.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>健康科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>航天</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>大数据</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新能源汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>软件与服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>房地产科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>教育科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>游戏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>机器人</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>网络安全</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新零售</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新能源</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>虚拟与增强现实</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>即时通讯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>即时通讯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3D印刷</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份       2009  ...    2011    2012    2013     2014    2015    2016   2017  \\\n",
       "行业               ...                                                           \n",
       "金融科技      210.0  ...  4040.0   990.0  2910.0  11570.0  1290.0   210.0  200.0   \n",
       "共享经济       70.0  ...   170.0  4600.0   420.0      NaN  1270.0   270.0  370.0   \n",
       "电子商务     1250.0  ...  1230.0  1900.0   170.0   1700.0   580.0   140.0   70.0   \n",
       "媒体和娱乐     150.0  ...  1350.0  5340.0   400.0    520.0   220.0     NaN    NaN   \n",
       "云计算       360.0  ...   670.0   880.0   650.0    540.0   370.0     NaN    NaN   \n",
       "物流          NaN  ...   740.0   770.0  2610.0   1180.0   360.0    70.0    NaN   \n",
       "人工智能      320.0  ...   440.0   360.0   440.0   1070.0   610.0  1310.0    NaN   \n",
       "消费品         NaN  ...     NaN   370.0     NaN    140.0  3570.0    70.0  150.0   \n",
       "生命科学      270.0  ...    70.0   220.0    70.0    800.0   300.0   550.0    NaN   \n",
       "健康科技      250.0  ...   410.0   340.0   340.0      NaN   140.0   600.0  150.0   \n",
       "区块链         NaN  ...    70.0   900.0  1300.0     70.0   150.0     NaN  300.0   \n",
       "航天          NaN  ...     NaN   200.0     NaN      NaN     NaN     NaN    NaN   \n",
       "大数据       100.0  ...   210.0   370.0   200.0     70.0   170.0     NaN    NaN   \n",
       "新能源汽车     350.0  ...     NaN     NaN     NaN   1070.0   520.0     NaN  590.0   \n",
       "软件与服务     140.0  ...    70.0   320.0   340.0    360.0     NaN     NaN    NaN   \n",
       "房地产科技       NaN  ...   440.0   240.0    70.0      NaN   370.0     NaN    NaN   \n",
       "教育科技        NaN  ...    70.0   270.0   270.0    340.0     NaN     NaN    NaN   \n",
       "游戏          NaN  ...     NaN   440.0     NaN      NaN   100.0     NaN    NaN   \n",
       "机器人         NaN  ...     NaN   300.0   100.0      NaN     NaN   200.0    NaN   \n",
       "网络安全       70.0  ...     NaN     NaN    70.0      NaN   200.0     NaN    NaN   \n",
       "新零售         NaN  ...   220.0     NaN   220.0      NaN   140.0     NaN   70.0   \n",
       "新能源        70.0  ...   220.0     NaN     NaN      NaN     NaN   150.0    NaN   \n",
       "虚拟与增强现实     NaN  ...   400.0    70.0     NaN      NaN     NaN     NaN    NaN   \n",
       "即时通讯       70.0  ...    70.0   220.0     NaN     70.0     NaN     NaN    NaN   \n",
       "3D印刷        NaN  ...    70.0     NaN   150.0      NaN   150.0     NaN    NaN   \n",
       "\n",
       "成立年份      2018   2019       总值  \n",
       "行业                              \n",
       "金融科技     200.0    NaN     金融科技  \n",
       "共享经济       NaN    NaN     共享经济  \n",
       "电子商务       NaN    NaN     电子商务  \n",
       "媒体和娱乐      NaN    NaN    媒体和娱乐  \n",
       "云计算        NaN    NaN      云计算  \n",
       "物流         NaN    NaN       物流  \n",
       "人工智能       NaN    NaN     人工智能  \n",
       "消费品      150.0    NaN      消费品  \n",
       "生命科学       NaN    NaN     生命科学  \n",
       "健康科技      70.0  100.0     健康科技  \n",
       "区块链        NaN    NaN      区块链  \n",
       "航天         NaN    NaN       航天  \n",
       "大数据        NaN    NaN      大数据  \n",
       "新能源汽车      NaN    NaN    新能源汽车  \n",
       "软件与服务      NaN    NaN    软件与服务  \n",
       "房地产科技    600.0    NaN    房地产科技  \n",
       "教育科技       NaN    NaN     教育科技  \n",
       "游戏         NaN    NaN       游戏  \n",
       "机器人        NaN    NaN      机器人  \n",
       "网络安全       NaN   70.0     网络安全  \n",
       "新零售       70.0    NaN      新零售  \n",
       "新能源        NaN    NaN      新能源  \n",
       "虚拟与增强现实    NaN    NaN  虚拟与增强现实  \n",
       "即时通讯       NaN    NaN     即时通讯  \n",
       "3D印刷       NaN    NaN     3D印刷  \n",
       "\n",
       "[25 rows x 21 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_pivot['总值']=data_pivot_排序\n",
    "new_data_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28ccb4e8be0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\liwen\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "data_pivot.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
